Understanding and Reasoning in Structured and Symbolic Representations

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.

Trustworthy Machine Learning for Al Safety and Al-driven Scientific Discovery

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?

Diagnosing NLP: Sources of Social Harms of NLP

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.

MuZero – Dynamic Learning for LLM Dialog Planning

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.

Aligning existing information-seeking processes with Conversational Information Seeking And much more

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, providing a foundation for the practical insights that follow. The presentation then explores real-world user engagements through interaction log analysis, revealing key patterns and behaviours. The focus shifts to the horizon of information retrieval, with innovative concepts in immersive information seeking. These visionary ideas represent the future of knowledge access.