In the field of Data Perspectivism, perspective has emerged as an umbrella term encompassing annotators’ points of view and culturally shaped worldviews. When modeling annotators, researchers have explored a variety…

The study of harms in NLP is a fast-evolving field of research, which in a few years has seen the need of considering the subjectivity that characterizes this phenomenon. In…

The emergence of large language models has transformed the landscape of conversational systems, but our understanding of how users interact with these systems and what they seek to accomplish remains…

Parkinson’s disease (PD) is a progressive neurodegenerative disorder with a lengthy prodromal phase that remains difficult to capture using traditional clinical tools. Most monitoring begins only after diagnosis, limiting insight…

The increasing burden of mental health disorders-including depression, anxiety, OCD, and suicidal ideation-necessitates the development of advanced Al frameworks capable of interpreting complex emotional signals from language. Our research focuses…

Amid the recent uptake of Generative Al, sociotechnical scholars and critics have traced a multitude of resulting harms, with analyses largely focused on values and axiology (e.g., bias). While value-…

Adversarial text-carefully crafted inputs designed to mislead or degrade the performance of NLP systems-poses a growing challenge across a range of language technologies. In this talk, I will present my…

When people comprehend, interpret, or communicate about their environment, they draw on “mental schemata” that encode common knowledge and associations based on experiences, moral values, or beliefs. New information that…

No artificial intelligence (Al) has yet been scientifically recognized as sentient. However, the concept of “sentient Al” continues to evoke a spectrum of fears-from valid concerns to misconceptions shaped by…

Recent large language models (LLMs) support long contexts ranging from 128K to 1M tokens. A popular method for evaluating these capabilities is the needle-in-a-haystack (NIAH) test, which involves retrieving a…

Over the past decade, Natural Language Processing (NLP) has undergone a transformative journey, marked by profound changes, particularly in the development of Large Language Models (LLMs). While some applications of…

Large Language Models (LLMs) are often seen as powerful yet static entities, their knowledge frozen after training, disconnected from the ever-evolving world. In this talk, we will explore the challenge…

The integration of machine learning, particularly large language models (LLMs), into medical applications offers great potential to conduct clinical documentation. This study explores the feasibility and effectiveness of generating structured…

Large language models (LLMs) have demonstrated remarkable capabilities, but their high computational costs and reliance on extensive labeled data limit their practical deployment in resource-constrained settings. This talk explores strategies…

Scaling up Language Models has led to increasingly advanced capabilities for those who can afford to train them. In order to enable community-tailored models for the rest of us, we…

In this talk, / will present CARAG, a Context-Aware Retrieval Augmented Generation framework that improves Automated Fact Verification (AFV) by incorporating both local and global explanations. Unlike traditional factchecking methods…

In the first part of my talk, I will discuss the joys and challenges of my master’s research on generating the script of a full-length play using GPT-2. Namely, I…

In this lecture, we will journey through the core principles of Al agents, building a conceptual bridge from foundational theories to cutting-edge practical implementations. Attendees will gain insights into how…

The Teuken 7B model, a large language model for *European languages*, has recently made the news. If you’re interested in knowing how such models are trained, this week’s speaker is…

We envision a world where Al agents (assistants) are widely used for complex tasks in our digital and physical worlds and are broadly integrated into our society. To move towards…

This talk outlines my research trajectory in language understanding and reasoning. I begin with event extraction through question-answering techniques, followed by constructing event schemas. Subsequently, I investigate the translation of…

Machine learning models, while effective in controlled environments, can fail catastrophically when exposed to unexpected conditions upon deployment. This lack of robustness, well-documented even in state-of-the-art models, can lead to…

The advances in language technologies has seen attempts at addressing increasingly complex tasks such as hate speech detection, in addition to longstanding tasks such as language generation and summarization. However,…

While large language models (LLMs) perform well on a variety of language-related tasks, they struggle with tasks that require planning. We apply the existing MuZero algorithm to enhance the planning…

This talk explores the theoretical aspects of Conversational Information Seeking (CIS) while combining ongoing interaction log analysis and envisioning future research. This talk begins with the core theories underpinning CIS,…