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 will examine cross-model consistencies in how LMs acquire their linguistic knowledge-from fundamental syntax and semantics up to higher-level pragmatic features, such as culture. By identifying these consistencies across different models, we highlight opportunities for how they can enable dynamic personalization approaches that improve the accessibility of language technologies for underserved communities, in which collecting sufficient training data is physically impossible.
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 that focus on isolated claims, CARAG leverages thematic embedding aggregation to verify claims in a broader contextual landscape. I will also introduce CARAG-u, an unsupervised extension that eliminates the need for predefined thematic annotations, dynamically deriving contextually relevant evidence clusters from unstructured data. CARAG-u maintains strong performance while increasing adaptability and scalability. Through benchmarks on the FactVer dataset, / will demonstrate how these frameworks enhance explainability and thematic coherence, advancing the role of Al in trustworthy, transparent fact verification.
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 will share some of the strategies we used to navigate around the limited context length of the model, getting the characters to have a consistent persona, and above everything else, making the play interesting to watch for the audience. In the second part, / will share my ongoing doctoral research on evaluating natural language generation. / will discuss our work on data contamination, present an overview of how NG is evaluated across different specific tasks, and share my challenges of evaluating the semantic accuracy of summarization at a scale when no reference is available.
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 autonomous agents operate, starting with basic Al agent architectures and evolving into sophisticated web automation systems. Highlighting our latest research with WebPilot, the lecture will showcase how integrating Monte Carlo Tree Search with a dual optimization strategy addresses the complexities of dynamic web tasks-mitigating vast action spaces and uncertainty through strategic exploration and adaptive decision-making.
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 one of the lead scientists who’s done it.
As part of the Lamarr NLP monthly meetings, this week we have the pleasure to host Dr. Mehdi Ali from the Fraunhofer IAIS who will give a guest lecture on How To Train A Multilingual Large Language Model?.
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 such a future, we need an environment for a robust evaluation of agents’ capability, reliability, and
trustworthiness.
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 natural language into symbolic representations to facilitate faithful reasoning. Currently, my work explores training language models using both natural language and knowledge graphs, as well as evaluating narratives through knowledge graphs.
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 severe harm in high-stakes, safety-critical application domains such as healthcare and to bias and inefficiencies in Al-driven scientific discovery. This shortcoming raises a central question: How can we develop machine learning models we can trust?
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, in spite of the advances and increased public and research attention to such tasks, language technologies broadly still broadly and widely cause social harms such as the propagation of social biases (in increasingly sensitive areas.
In this talk, I will discuss sources of biases and suggested technical interventions, in order to identity whether they address the underlying issues. In particular, I will attend to the political reality of how language technologies are deployed and what their use is. Through this discussion, I hope to highlight pathways for research on language technologies to be used in service of society.
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 capabilities of LLMs in dialog settings. MuZero uses a neural network to represent observations into a latent space, and then performs Monte Carlo tree search in the latent space using dynamics learned through self-play. We develop a simulated dialog environment to train the MuZero-based model on conversations with a generative LLM such as DialoGPT. We also investigate modifications to the model architecture, such as replacing the representation network by a transformer pretrained on sentence classification. We evaluate our algorithm on realistic multi-turn dialog planning tasks, such as steering the dialog topic to a predefined goal.
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