Emergent misalignment (EM) is the surprising tendency of language models to become broadly misaligned after fine-tuning on narrowly misaligned examples. While EM has been extensively studied in the supervised fine-tuning (SFT) setting, evidence that it also arises from reinforcement learning (RL) is limited to large, closed-source models, leaving the phenomenon expensive to study and difficult...

Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large statistics at reduced realism. In this work, we systematically study transfer learning between fast-simulated and fully simulated datasets in a realistic LHC environment. We consider three representative tasks, signal-background classification, quark-gluon jet...

Accurate Monte Carlo (MC) modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observed data. In practice, experimental information is often limited to one-dimensional (1D) distributions, while mismodelling arises in a multidimensional feature space. This restricts traditional correction methods, as one-dimensional reweighting ignores correlations and fully multidimensional approaches require...

Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flexibility enables unprecedented performance in high-energy physics (HEP) analyses, it also makes them sensitive to small variations in their inputs. Consequently, the propagation and estimation of systematic uncertainties in NN-based models remain an open challenge. There are indications...

Agentic AI systems are increasingly being integrated into scientific workflows, yet their behavior under realistic conditions remains insufficiently understood. We evaluate CMBAgent across two workflow paradigms and eighteen astrophysical tasks. In the One-Shot setting, access to domain-specific context yields an approximately ~6x performance improvement (0.85 vs. ~0 without context), with the primary failure mode being...

Reasoning in large language models is often treated as a monolithic capability, but its observed gains may arise from more basic operations. We study reasoning through two such primitives, recall and state-tracking, and ask whether hybrid architectures that combine attention-based retrieval with recurrent state updates are better suited than attention-only models for tasks that jointly...

The EmpathiCH workshop series has, over three iterations, unpacked how empathy is conceptualized, measured, and used in HCI, identifying both its potential benefits and notable pitfalls. Despite these discussions, the diverse roles of empathy in research and practice remain fragmented and under-theorized. This fourth iteration seeks to consolidate perspectives by situating empathy within a sociomaterial...

Due to the scarcity of labeled dialectal speech, audio dialect classification is a challenging task for most languages, including Swiss German. In this work, we explore the ability of large language models (LLMs) as agents in understanding the dialects and whether they can show comparable performance to models such as HuBERT in dialect classification. In...

Conspiracy theories are anti-authoritarian narratives that lead to social conflict, impacting how people perceive political information. To help in understanding this issue, we introduce the Conspiracy Frame: a fine-grained semantic representation of conspiratorial narratives derived from frame-semantics and semiotics, which spawned the Conspiracy Frames (Con.Fra.) dataset: a corpus of Telegram messages annotated at span-level. The...

How people seek, request, and exchange information in social interactions is shaped by personality and situational context, connecting the fields of interactive information science and attribution theory in social psychology. In everyday life, people seek information to achieve goals, collaborate, and manage social conflicts. Understanding how individual traits and contextual factors influence information-seeking behavior remains...

In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our understanding of fundamental phenomena. Increasingly, deep learning models are applied to analyze both simulated and experimental data. In most experiments, a rigorous regime of testing for physically motivated systematic uncertainties is in...

We present Tucano 2, a fully open suite of large language models (LLMs) with 0.5-3.7 billion parameters, designed to address certain gaps in open-source development for Portuguese LLMs. Following our previous works, we now extend our dataset, GigaVerbo-v2, to a new degree of quality and scale, while also introducing a new synthetic dataset, GigaVerbo-v2 Synth,...

The dominance of large multilingual foundation models has widened linguistic inequalities in Natural Language Processing (NLP), often leaving low-resource languages underrepresented. This paper introduces LilMoo, a 0.6-billion-parameter Hindi language model trained entirely from scratch to address this gap. Unlike prior Hindi models that rely on continual pretraining from opaque multilingual foundations, LilMoo is developed through...

We present Tucano 2, a fully open suite of large language models (LLMs) with 0.5-3.7 billion parameters, designed to address certain gaps in open-source development for Portuguese LLMs. Following our previous works, we now extend our dataset, GigaVerbo-v2, to a new degree of quality and scale, while also introducing a new synthetic dataset, GigaVerbo-v2 Synth,...

We propose an agentic data augmentation method for Aspect-Based Sentiment Analysis (ABSA) that uses iterative generation and verification to produce high-quality synthetic training examples. To isolate the effect of agentic structure, we also develop a closely matched prompting-based baseline using the same model and instructions. Both methods are evaluated across three ABSA subtasks—Aspect Term Extraction...

Theory of Mind (ToM) refers to an agent's ability to model the internal states of others. Contributing to the debate whether large language models (LLMs) exhibit genuine ToM capabilities, our study investigates their ToM robustness using perturbations on false-belief tasks and examines the potential of Chain-of-Thought prompting (CoT) to enhance performance and explain the LLM's...

Pluralism, the capacity to engage with diverse perspectives without collapsing them into a single viewpoint, is critical for developing large language models that faithfully reflect human heterogeneity. Yet this characteristic has not been carefully examined in the LLM research community and remains absent from most alignment studies. Debate-oriented sources provide a natural entry point for...

Recent advances in large language model (LLM) pruning have shown state-of-the-art (SotA) compression results in post-training and retraining-free settings while maintaining high predictive performance. However, previous research mainly considered calibrating based on English text, despite the multilingual nature of modern LLMs and their frequent use in non-English languages. This analysis paper conducts an in-depth investigation...

How people seek, request, and exchange information in social interactions is shaped by personality and situational context, connecting the fields of interactive information science and attribution theory in social psychology. In everyday life, people seek information to achieve goals, collaborate, and manage social conflicts. Understanding how individual traits and contextual factors influence information-seeking behavior remains...

Large Language Models are used globally but are often aligned to primarily Western values. To better understand the need for pluralistic alignment methods, this paper presents a pilot survey that investigates how end users from diverse cultural contexts perceive the representation of their values in AIs, their demand for models better aligned to their own...

Scientific literature in astronomy is rapidly expanding, making it increasingly important to automate the extraction of key entities and contextual information from research papers. In this paper, we present an encoder-based system for extracting knowledge from astronomy articles. Our objective is to develop models capable of classifying telescope references, detecting auxiliary semantic attributes, and recognizing...

Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high energy physics, one common deep learning use-case is the classification of signal and background processes in particle collisions. In many such cases, the fundamental principles of the correlations...

Arabic, one of the world’s most widely spoken languages, is marked by extensive dialectal variation that often differs significantly from Modern Standard Arabic (MSA) and from other dialects. This linguistic diversity presents considerable challenges for machine translation systems, especially when translating dialectal Arabic into MSA or English. Addressing this gap, this work introduces TARGAMA, a...

When LLM agents work together, they seem to be more powerful than a single LLM in mathematical question answering. However, are they also more robust to adversarial inputs? We investigate this question using adversarially perturbed math questions. These perturbations include punctuation noise with three intensities (10, 30, and 50 percent), plus real-world and human-like typos...

In this paper, we present a new algorithm, MiniFool, that implements physics-inspired adversarial attacks for testing neural network-based classification tasks in particle and astroparticle physics. While we initially developed the algorithm for the search for astrophysical tau neutrinos with the IceCube Neutrino Observatory, we apply it to further data from other science domains, thus demonstrating...

Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one another and capture properties of empathy observable in text remains unclear. To provide insight into this, we analyze the transfer performance of empathy models adapted to empathy tasks with...

Despite advances in conversational systems, the evaluation of such systems remains a challenging problem. Current evaluation paradigms often rely on costly homogeneous human annotators or oversimplified automated metrics, leading to a critical gap in socially aligned conversational agents, where pluralistic values (ie, acknowledging diverse human experiences) are essential to reflect the inherently subjective and contextual...

Continual pretraining promises to adapt large language models (LLMs) to new domains using only unlabeled test-time data, but naively applying standard self-supervised objectives to instruction-tuned models is known to degrade their instruction-following capability and semantic representations. Existing fixes assume access to the original base model or rely on knowledge from an external domain-specific database -...

Proceedings of the 18th International Natural Language Generation Conference Page 1 INLG 2025 Proceedings of the 18th International Natural Language Generation Conference System Demonstrations October 29 – November 2, 2025 Page 2 ©2025 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth...

We are excited to present the Proceedings of the 18th International Natural Language Generation Conference (INLG 2025). This year’s INLG takes place from October 29 to November 2, 2025 in Hanoi, Vietnam and is organized by the Vietnam Institute for Advanced Study in Mathematics. We would like to thank the local organizing team led by...

Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, raising concerns about the reliability and consistency of multilingual Large Language Models (LLMs). We systematically examine cross-lingual disparities in pre-training source and factuality alignment in LLM answers for multilingual healthcare Q&A across English, German, Turkish, Chinese...

We present an overview of the Colliding With Adversaries challenge on robust learning in High Energy Physics, held at ECML PKDD 2025. The challenge was split into two tasks: (1) generating adversarial examples to attack a deep learning model, and (2) developing a model robust to unseen adversarial attacks while maintaining strong performance on clean...

We investigate lightweight and easily applicable data augmentation techniques for dialectal audio classification. We evaluate four main methods, namely shifting pitch, interval removal, background noise insertion and interval swap as well as several subvariants on recordings from 20 German dialects. Each main method is tested across multiple hyperparameter combinations, inlcuding augmentation length, coverage ratio and...

We present a low-compute non-generative system for implementing interview-style conversational agents which can be used to facilitate qualitative data collection through controlled interactions and quantitative analysis. Use cases include applications to tracking attitude formation or behavior change, where control or standardization over the conversational flow is desired. We show how our system can be easily...

Large language models implicitly encode preferences over human values, yet steering them often requires large training data. In this work, we investigate a simple approach: Can we reliably modify a model's value system in downstream behavior by training it to answer value survey questions accordingly? We first construct value profiles of several open-source LLMs by...

Fine-tuning lets practitioners repurpose aligned large language models (LLMs) for new domains, yet recent work reveals emergent misalignment (EMA): Even a small, domain-specific fine-tune can induce harmful behaviors far outside the target domain. Even in the case where model weights are hidden behind a fine-tuning API, this gives attackers inadvertent access to a broadly misaligned...

While Large Language Models (LLMs) have shown impressive capabilities in math problem-solving tasks, their robustness to noisy inputs is not well-studied. We propose ArithmAttack to examine how robust the LLMs are when they encounter noisy prompts that contain extra noise in the form of punctuation marks. While being easy to implement, ArithmAttack does not cause...

Deep learning models for dialect identification are often limited by the scarcity of dialectal data. To address this challenge, we propose to use Retrieval-based Voice Conversion (RVC) as an effective data augmentation method for a low-resource German dialect classification task. By converting audio samples to a uniform target speaker, RVC minimizes speaker-related variability, enabling models...

Hyperbolic representations are effective in modeling knowledge graph data which is prevalently used to facilitate multi-hop reasoning. However, a rigorous and detailed comparison of the two spaces for this task is lacking. In this paper, through a simple integration of hyperbolic representations with an encoder-decoder model, we perform a controlled and comprehensive set of experiments...

We leveraged LLaMA, utilizing its ability to evaluate the relevance of retrieved claims within a retrieval-based fact-checking framework. This approach aimed to explore the impact of large language models (LLMs) on retrieval tasks and assess their effectiveness in enhancing fact-checking accuracy. Additionally, we integrated Jina embeddings v2 and the MPNet multilingual sentence transformer to filter...

Large language models (LLMs) often generate hallucinated content, making it crucial to identify and quantify inconsistencies in their outputs. We introduce HaluMap, a post-hoc framework that detects hallucinations by mapping entailment and contradiction relations between source inputs and generated outputs using a natural language inference (NLI) model. To improve reliability, we propose a calibration step...

The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the context of specific ideologies, our ability to accurately characterize extremism in more generalizable terms remains underdeveloped. In this paper, we propose a novel method for extracting and analyzing extremist discourse across...

High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at...

Online dissemination of conspiracy theories (CTs) during epidemics poses significant risks to public health. This paper addresses the problem of detecting CTs in social media posts with an emphasis on the resource-constrained scenarios characterized by the absence of labeled datasets and the high cost of expert annotation. To address these challenges, we investigate resource-efficient methods...

Equitable access to reliable health information is vital for public health, but the quality of online health resources varies by language, raising concerns about inconsistencies in Large Language Models (LLMs) for healthcare. In this study, we examine the consistency of responses provided by LLMs to health-related questions across English, German, Turkish, and Chinese. We largely...

Two core challenges of alignment are 1) scalable oversight and 2) accounting for the dynamic nature of human values. While solutions like recursive reward modeling address 1), they do not simultaneously account for 2). We sketch a roadmap for a novel algorithmic framework that trains a superhuman reasoning model to decompose complex tasks into subtasks...

This paper examines the effect of preprocessing techniques on spoken dialect classification using raw audio data. We focus on modifying Root Mean Square (RMS) amplitude, DC-offset, articulation rate (AR), pitch, and Harmonics-to-Noise Ratio (HNR) to assess their impact on model performance. Our analysis determines whether these features are important, irrelevant, or misleading for the classification...

Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one another and capture properties of empathy observable in text remains unclear. To provide insight into this, we analyze the transfer performance of empathy models adapted to empathy tasks with...

Large Language Models (LLMs) have shown impressive performance in various NLP tasks. However, there are concerns about their reliability in different domains of linguistic variations. Many works have proposed robustness evaluation measures for local adversarial attacks, but we need globally robust models unbiased to different language styles. We take a broader approach to explore a...

Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high energy physics, one of the common deep learning use-cases is the classification of signal and background processes in particle collisions. In many such cases, the fundamental principles of...

This paper presents a first look at CCEE (Complex Context-aware Event Extraction), a currently in the works novel evaluation dataset for context-rich gene regulatory network extraction from scientific literature. We propose an annotation scheme for cancer research papers, capturing both core gene interactions and extensive contextual information across 10-14 categories per event, addressing limitations in...

Propaganda, a pervasive tool for influenc-ing public opinion, demands robust auto-mated detection systems, particularly for under-resourced languages. Current efforts largely focus on well-resourced languages like English, leaving significant gaps in languages such as Arabic. This research addresses these gaps by introducing MultiProp Framework, a cross-lingual meta-learning framework designed to enhance propaganda detection across multiple languages,...

With the success of ChatGPT and other similarly sized SotA LLMs, claims of emergent human like social reasoning capabilities, especially Theory of Mind (ToM), in these models have appeared in the scientific literature. On the one hand those ToM-capabilities have been successfully tested using tasks styled similar to those used in psychology (Kosinski, 2023). On...

Narratives are widely recognized as a powerful tool for structuring information and facilitating comprehension of complex ideas in various domains such as science communication. This paper investigates whether incorporating narrative elements can assist Large Language Models (LLMs) in solving complex problems more effectively. We propose a novel approach, Story of Thought (SoT), integrating narrative structures...

Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals—even experts—resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the ‘wisdom of crowds’, online forums that...

We present the Proceedings of The 2nd Workshop on Practical LLM-assisted Data-to-Text (Practical D2T). This year’s Practical D2T takes place at INLG 2024 on Sept 23 in Tokyo, Japan. We would like to thank the INLG organisers for their support.Natural Language Generation (NLG) has been an active area of research for decades, both academically and...

Although language model performance across diverse tasks continues to improve, these models still struggle to understand and explain the beliefs of other people. This skill requires perspective-taking, the process of conceptualizing the point of view of another person. Perspective taking becomes challenging when the text reflects more personal and potentially more controversial beliefs. We explore...

A word’s meaning resides in the heart and soul of its “generator”-people. How do we include human (personal, social, cultural, situational) context, ethically, into LLMs–the base models of our NLP systems?Language modeling in the context of its source [author] and target [audience] can enable NLP systems to better understand human language. Advances in human-centered NLP...

While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training on bias mitigation, is lacking. This study addresses this gap by systematically training six LLMs of identical size (2.6 B parameters) and architecture: five monolingual models (English, German, French, Italian, and Spanish)...

Searching through conversational interactions has been emphasized as the next frontier. Nowadays, conversational agents can generate natural language responses, transforming how we search for information. A key challenge in conversational information-seeking is how these agents present information: should they only reflect facts, cater to human cognitive preferences, or strike a balance between them? These challenges...

Empathetic response generation is a desirable aspect of conversational agents, crucial for facilitating engaging and emotionally intelligent multi-turn conversations between humans and machines. Leveraging large language models for this task has shown promising results, yet challenges persist in ensuring both the empathetic quality of the responses and retention of the generalization performance of the models....

Studies on detecting and understanding the spread of unreliable news on social media have identified key characteristic differences between reliable and unreliable posts. These differences in language use also vary in expression across individuals, making it important to consider personal factors in unreliable news detection. The application of personalization methods for this has been made...

Recent trends in natural language processing research and annotation tasks affirm a paradigm shift from the traditional reliance on a single ground truth to a focus on individual perspectives, particularly in subjective tasks. In scenarios where annotation tasks are meant to encompass diversity, models that solely rely on the majority class labels may inadvertently disregard...

With growing interest in the use of large language models, it is becoming increasingly important to understand whose views they express. These models tend to generate output that conforms to majority opinion and are not representative of diverse views. As a step toward building models that can take differing views into consideration, we build a...

Empathetic leadership communication plays a pivotal role in modern workplaces as it is associated with a wide range of positive individual and organizational outcomes. This paper introduces LeadEmpathy, an innovative expert-annotated German dataset for modeling empathy in written leadership communication. It features a novel theory-based coding scheme to model cognitive and affective empathy in asynchronous...

In today’s rapidly evolving digital age, disinformation poses a significant threat to public sentiment and socio-political dynamics. To address this, we introduce a new dataset “DeFaktS”, designed to understand and counter disinformation within German media. Distinctively curated across various news topics, DeFaktS offers an unparalleled insight into the diverse facets of disinformation. Our dataset, containing...

Empathy is essential in healthcare communication. We introduce an annotation approach that draws on well-established frameworks for clinical empathy and breaking bad news (BBN) conversations for considering the interactive dynamics of discourse relations. We construct Empathy in BBNs, a span-relation task dataset of simulated BBN conversations in German, using our annotation scheme, in collaboration with...

Research on psychological risk factors for suicide has developed for decades. However, combining explainable theory with modern data-driven language model approaches is non-trivial. In this study, we propose and evaluate methods for identifying language patterns aligned with theories of suicide risk by combining theory-driven suicidal archetypes with language model-based and relative entropy-based approaches. Archetypes are...

Emotions are fundamentally integral to shaping the order and disorders in human lives. Yet, a principled, quantitative framework explaining emotional dynamics and their alteration in mental disorders has been elusive. This challenge arises from the complex and multidimensional nature of emotions but also, at least partially, due to a shortage of large longitudinal measurements and...

Analyzing user opinion changes in long conversation threads is extremely critical for applications like enhanced personalization, market research, political campaigns, customer service, targeted advertising, and content moderation. Unfortunately, previous studies on stance and dogmatism in user conversations have focused on training models using datasets annotated at the post level, treating each post as independent and...

Sarcasm detection is a challenging task for various NLP applications. It often requires additional context related to the conversation or participants involved to interpret the intended meaning. In this work, we introduce an extended reactive supervision method to collect sarcastic data from Twitter and improve the quality of the data that is extracted. Our new...

Recent language models have been improved by the addition of external memory. Nearest neighbor language models retrieve similar contexts to assist in word prediction. The addition of locality levels allows a model to learn how to weight neighbors based on their relative location to the current text in source documents, and have been shown to...

The potential of medical domain dialogue agents lies in their ability to provide patients with faster information access while enabling medical specialists to concentrate on critical tasks. However, the integration of large-language models (LLMs) into these agents presents certain limitations that may result in serious consequences. This paper investigates the challenges and risks of using...

When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products. However, training an AI for such conversations...

This research contributes to the task of predicting empathy and personality traits within dialogue, an important aspect of natural language processing, as part of our experimental work for the WASSA 2023 Empathy and Emotion Shared Task. For predicting empathy, emotion polarity, and emotion intensity on turns within a dialogue, we employ adapters trained on social...

Class imbalance problem can cause machine learning models to produce an undesirable performance on the minority class as well as the whole dataset. Using data augmentation techniques to increase the number of samples is one way to tackle this problem. We introduce a novel counterfactual data augmentation by verb replacement for the identification of medical...

Clinical NLP tasks such as mental health assessment from text, must take social constraints into account-the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to'what is necessary'for a...

Check-worthiness detection is the task of identifying claims, worthy to be investigated by fact-checkers. Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual check-worthiness detection.This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in...

We review the state of research on empathy in natural language processing and identify the following issues:(1) empathy definitions are absent or abstract, which (2) leads to low construct validity and reproducibility. Moreover,(3) emotional empathy is overemphasized, skewing our focus to a narrow subset of simplified tasks. We believe these issues hinder research progress and...

Recent language modeling performance has been greatly improved by the use of external memory. This memory encodes the context so that similar contexts can be recalled during decoding. This similarity depends on how the model learns to encode context, which can be altered to include other attributes, such as style. We construct and evaluate an...

Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators. However, often little or no information about annotators is known, or the set of annotators is small. In this work, we examine a corpus of social media...

Framing has become recognised as a powerful communication strategy for winning debates and shaping opinions and decisions. Entman defines framing as an action of selecting “some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation,...

Studies on interpersonal conflict have a long history and contain many suggestions for conflict typology. We use this as the basis of a novel annotation scheme and release a new dataset of situations and conflict aspect annotations. We then build a classifier to predict whether someone will perceive the actions of one individual as right...

We propose adversarial methods for increasing the robustness of disease mention detection on social media. Our method applies adversarial data augmentation on the input and the embedding spaces to the English BioBERT model. We evaluate our method in the SocialDisNER challenge at SMM4H’22 on an annotated dataset of disease mentions in Spanish tweets. We find...

With synthetic data generation, the required amount of human-generated training data can be reduced significantly. In this work, we explore the usage of automatic paraphrasing models such as GPT-2 and CVAE to augment template phrases for task-oriented dialogue systems while preserving the slots. Additionally, we systematically analyze how far manually annotated training data can be...

Social media enable users to share their feelings and emotional struggles. They also offer an opportunity to provide community support to suicidal users. Recent studies on suicide risk assessment have explored the user's historic timeline and information from their social network to analyze their emotional state. However, such methods often require a large amount of...

Social media platforms such as Twitter or Reddit have become an integral part in political opinion formation and discussions, accompanied by potential echo chamber forming. In this paper, we examine the relationships between the interaction patterns, the opinion polarity, and the socio-demographic characteristics in discussion communities on Reddit. On a dataset of over 2 million...

Large pre-trained neural language models have supported the effectiveness of many NLP tasks, yet are still prone to generating toxic language hindering the safety of their use. Using empathetic data, we improve over recent work on controllable text generation that aims to reduce the toxicity of generated text. We find we are able to dramatically...

Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. In this paper, we introduce a new contemporary Reddit dataset for fake news spreader analysis, called FACTOID, monitoring political discussions on Reddit since the beginning of 2020. The dataset contains over 4K users with 3.4 M Reddit...

There is an increasing need for the ability to model fine-grained opinion shifts of social media users, as concerns about the potential polarizing social effects increase. However, the lack of publicly available datasets that are suitable for the task presents a major challenge. In this paper, we introduce an innovative annotated dataset for modeling subtle...

Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities. We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space. DMix leverages the hyperbolic space as a similarity measure among input samples...

We build a system that leverages adapters, a light weight and efficient method for leveraging large language models to perform the task Em-pathy and Distress prediction tasks for WASSA 2022. In our experiments, we find that stacking our empathy and distress adapters on a pre-trained emotion lassification adapter performs best compared to full fine-tuning approaches...

Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowledge, in particular when covering a large variety of diseases. Such knowledge can be coded in a knowledge graph -- encompassing diseases, symptoms, and diagnosis paths....

We report goals, paper submissions, keynotes, and organizations of this UserNLP workshop. User-centered NLP can fill these gaps by explicitly considering stylistic variations across individuals or groups of individuals and focusing on user-level modeling tasks. While traditional NLP tasks tend to focus on single documents (e.g., sentiment analysis), user-centered NLP aims to make inferences for...

The performance cost of differential privacy has, for some applications, been shown to be higher for minority groups; fairness, conversely, has been shown to disproportionally compromise the privacy of members of such groups. Most work in this area has been restricted to computer vision and risk assessment. In this paper, we evaluate the impact of...

Framing has become recognised as a powerful communication strategy for winning debates and shaping opinions and decisions. Entman defines framing as an action of selecting “some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation,...

Interpolation-based regularisation methods have proven to be effective for various tasks and modalities. Mixup is a data augmentation method that generates virtual training samples from convex combinations of individual inputs and labels. We extend Mixup and propose DMix, distance-constrained interpolative Mixup for sentence classification leveraging the hyperbolic space. DMix achieves state-of-the-art results on sentence classification...

Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities. These methods involve performing mathematical operations over the raw input samples or their latent states representations-vectors that often possess complex hierarchical geometries. However, these operations are performed in the Euclidean space, simplifying these representations, which may lead to distorted...

We introduce the problem of proficiency modeling: Given a user's posts on a social media platform, the task is to identify the subset of posts or topics for which the user has some level of proficiency. This enables the filtering and ranking of social media posts on a given topic as per user proficiency. Unlike...

Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with...

Natural Language Generation (NLG) has received much attention with rapidly developing models and ever-more available data. As a result, a growing amount of work attempts to personalize these systems for better human interaction experience. Still, diverse sets of research across multiple dimensions and numerous levels of depth exist and are scattered across various communities. In...

Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly leveraging a user’s emotional history and social information from a user’s neighborhood...

Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Contextualizing the build-up of such ideation is critical for the identification of users at risk. In this work, we focus on identifying suicidal intent in tweets by augmenting linguistic models with emotional phases modeled from users’...

Most NLP models today treat language as universal, even though socio-and psycholingustic research shows that the communicated message is influenced by the characteristics of the speaker as well as the target audience. This paper surveys the landscape of personalization in natural language processing and related fields, and offers a path forward to mitigate the decades...

This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions. Specifically, we propose to develop and operate a prototype conversational search system for scholarly activities. This Scholarly Conversational Assistant would serve as a useful tool, a...

Online, social media communication is often ambiguous, and it can encourage speed and inattentiveness. We investigated whether Actively Open Minded Thinking (AOT), a dispositional willingness to seek out new or potentially threatening information, may help users avoid these pitfalls. In Study 1, we determined that correctly assessing social media authors’ traits was positively predicted by...

Previous research has linked psychological and social variables to physical health. At the same time, psychological and social variables have been successfully predicted from the language used by individuals in social media. In this paper, we conduct an initial exploratory study linking these two areas. Using the social media platform of Twitter, we identify users...

The 2nd Workshop on Stylistic Variation (StyVa) at NAACL 2018 follows up the successful first iteration of the workshop at EMNLP 2017. The goal of the workshop is to offer a venue for bringing together a large but previously underserved and splintered community within computational linguistics, attracting a variety of perspectives on style from traditional...

Obtaining insights into the style and content characteristics of a novel can provide a benefit to a large number of users. Parents and teachers may be interested in finding appropriate books for children. Booksellers may want to assess the fit of a candidate’s artwork into their portfolio or determine the target audience for their promotion...

In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (eg, part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose...

Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting...

People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess...

This dissertation is concerned with the applicability of knowledge, contained in lexicalsemantic resources, to text classification tasks. Lexical-semantic resources aim at systematically encoding various types of information about the meaning of words and their relations. Text classification is the task of sorting a set of documents into categories from a predefined set, for example,“spam” and...

Scientific reasoning and argumentation (SRA) is a complex process. Thus, analyzing the quality of learners’ SRA and presenting SRA outcomes are important research problems. This study attempts to account for these problems by developing a user interface that facilitates learning scientists to analyze SRA, enabling them to evaluate the performance of an automated coding algorithm...

Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation. To date, no effort has been put into integrating the supersenses into distributional word representations. We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words...

Writing style allows NLP tools to adjust to the traits of an author. In this paper, we explore the relation between stylistic and syntactic features and authors’ age and income. We confirm our hypothesis that for numerous feature types writing style is predictive of income even beyond age. We analyze the predictive power of writing...

User traits disclosed through written text, such as age and gender, can be used to personalize applications such as recommender systems or conversational agents. However, human perception of these traits is not perfectly aligned with reality. In this paper, we conduct a large-scale crowdsourcing experiment on guessing age and gender from tweets. We systematically analyze...

Physics beyond the Standard Model predicts the possible existence of new particles that can be searched at the low-energy frontier in the sub-eV range. The OSQAR photon regeneration experiment looks for “light shining through a wall” from the quantum oscillation of optical photons into “weakly interacting sub-eV particles,” such as axion or axionlike particles (ALPs)...

Contemporary sentiment analysis approaches rely heavily on lexicon based methods. This is mainly due to their simplicity, although the best empirical results can be achieved by more complex techniques. We introduce a method to assess suitability of generic sentiment lexicons for a given domain, namely to identify frequent bigrams where a polar word switches polarity....

This study focuses on personality prediction of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character personality and design our task as a text classification problem. We incorporate a range of semantic features, including WordNet and VerbNet sense-level information and word vector...

Analyzing and assessing the quality of classroom lessons on a range of quality dimensions is a number one educational research topic, as this allows developing teacher trainings and interventions to improve lesson quality. We model this assessment as a text classification task, exploiting linguistic features to predict the scores in several lesson quality dimensions relevant...

For 2015, the OSQAR collaboration will focus on a new proposal for the search of chameleon, a hypothetical scalar particle postulated as a dark energy candidate with an environment-dependant mass. The required experimental set-up has been successfully tested and validated in 2014 at the SM-18 experimental hall. This proposal will focus on the sensitivity that...

Sentiment prediction from Twitter is of the utmost interest for research and commercial organizations. Systems are usually using lexicons, where each word is positive or negative. However, word lexicons suffer from ambiguities at a contextual level: the word dark is positive in dark chocolate and negative in dark soul, the word lost is positive with...

Analyzing large-bodies of audiovisual information with respect to discoursepragmatic categories is a time-consuming, manual activity, yet of growing importance in a wide variety of domains. Given the transcription of the audiovisual recordings, we propose to model the task of assigning discoursepragmatic categories as supervised machine learning task. By analyzing the effects of a wide variety...

There are many applications of text classification such as gender attribution in market research or the identification of forged product reviews on e-commerce sites. Although several automatic methods provide satisfying performance in most application cases, we see a gap in supporting the analyst to understand the results and derive knowledge for future application scenarios. In...

Social media allows any user to express themselves to the public through posting content. Using a crowdsourcing experiment, we aim to quantify and analyze which human attributes lead to better perceptions of the true identity of others. Using tweet content from a set of users with known age and gender information, we ask workers to...

The OSQAR photon regeneration experiment searches for pseudoscalar and scalar axion-like particles by the method of "Light Shining Through a Wall", based on the assumption that these weakly interacting sub-eV particles couple to two photons to give rise to quantum oscillations with optical photons in strong magnetic field. No excess of events has been observed,...

Recent theoretical and experimental studies highlight the possibility of new fundamental particle physics beyond the Standard Model that can be probed by sub-eV energy experiments. The OSQAR photon regeneration experiment looks for “Light Shining through a Wall” from the quantum oscillation of optical photons into “Weakly Interacting Sub-eV Particles”, like axion or axion-like particles (ALPs), in...

We present a sentiment classification system that participated in the SemEval 2014 shared task on sentiment analysis in Twitter. Our system expands tokens in a tweet with semantically similar expressions using a large novel distributional thesaurus and calculates the semantic relatedness of the expanded tweets to word lists representing positive and negative sentiment. This approach...

With more than 22 million articles, the largest collaborative knowledge resource never sleeps, experiencing several article edits every second. Over one fifth of these articles describes individual people, the majority of which are still alive. Such articles are, by their nature, prone to corruption and vandalism. Manual quality assurance by experts can barely cope with...

The corpus lists article IDs of biographies of living and dead people, rated as above average or below average along four categories (trustowrthy, objective, well written, complete) based on the ratings from Wikipedia Article Feedback v4 [http://en. wikipedia. org/wiki/Wikipedia: Article_Feedback_Tool](each of the listed articles rated at least 10 times).

Laser-based experiment OSQAR in CERN is aimed to the search of the axions by two methods. The photon regeneration experiment is using two LHC dipole magnets of the length 14.3m and magnetic field 9.5T equipped with an optical barrier at the end of the first magnet. It looks as light shining through the wall. No...

Recent intensive theoretical and experimental studies highlight the possibility of new fundamental particle physics beyond the standard model that can be probed by sub-eV energy experiments. The OSQAR photon regeneration experiment looks for Light Shining through a Wall (LSW) from the quantum oscillation of optical photons into Weakly Interacting Sub-eV Particles (WISPs), like axion or...

Would you target your audience differently, knowing the real age and gender of the text authors on your website forum? This paper examines hundreds of thousands of online documents, eg chat lines or blog posts, showing that computers are capable to address this task better than humans, without relying on content stereotypes. Pointing out that...

Recent intensive theoretical and experimental studies shed light on possible new physics beyond the standard model of particle physics, which can be probed with sub-eV energy experiments. In the second run of the OSQAR photon regeneration experiment, which looks for the conversion of photon to axion (or Axion-Like Particle), two spare superconducting dipole magnets of...

Recent intensive theoretical and experimental studies shed light on possible new physics beyond the standard model of particle physics, which can be probed with sub-eV energy experiments. In the second run of the OSQAR photon regeneration experiment, which looks for the conversion of photon to axion (or Axion-Like Particle), two spare superconducting dipole magnets of...

Continual pretraining promises to adapt large language models (LLMs) to new test domains using only unlabeled data, but naively applying common self-supervised objectives is known to degrade instruction-following performance. Existing fixes assume access to the original base model-a realistic barrier in settings where the base model weights are withheld for safety reasons. In this work,...

Theory of Mind (ToM) is considered essential in understanding the intentions and beliefs of others. Recent advancements in large language models (LLMs) like ChatGPT have sparked claims that these models exhibit ToM capabilities. However, follow-up studies reveal that these capabilities vanish with slight task variations. This paper introduces a novel dataset comprising 68 tasks across...

Implicit hate speech is defined by coded or indirect language that disparages a person or group on the basis of protected characteristics like race, gender, and cultural identity. Compared to explicit hate speech detection, implicit hate speech contains several challenges for the NLP models. One key challenge, is that implicit hate speech detection does not...

With synthetic data generation, the required amount of human-generated training data can be reduced significantly. In this work, we explore the usage of automatic paraphrasing models such as GPT-2 and CVAE to augment template phrases for task-oriented dialogue systems while preserving the slots. Additionally, we systematically analyze how far manually annotated training data can be...

Detecting fake news online is an important and timely issue. We are interested in investigating personal differences in how fake news is spread and developing models to detect fake news. Our group has collected data for this task and for thousands of Reddit users. This can be used to test models for personalization from embeddings,...

Language modeling forms the foundation of many language processing problems and involves predicting which words come next in a sequence. Recent work on kNN language models has shown improved perplexity by storing encodings of sentence contexts and retrieving similar contexts to alter the probability distribution when predicting the next token (Khandelwal et al. 2020). If...

While social media platforms help to connect people worldwide and give access to enormous amounts of diverse information, they also foster an environment that promotes polarization. This occurs due to the fact that users show a tendency to consume content that aligns with their political leaning and join groups adhering to their beliefs. This phenomenon...

Experiments in the field of particle physics produce vast amounts of data which need to be stored and processed. This means that reliable and efficient data acquisition system (DAQ) is an integral part of these experiments. COMPASS is a fixed target experiment operating on the Super Proton Synchrotron particle accelerator in CERN (European Organization for...