Specializing LLMs for Factuality and Soft Reasoning

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• Greg Durrett, UT Austin
• Thursday 17 October 2024, 14:00-15:00
• https://cam-ac-uk.zoom.us/j/97599459216?pwd=QTRsOWZCOXRTREVnbTJBdXVpOXFvdz09.

If you have a question about this talk, please contact Tiancheng Hu.

Proponents of LLM scaling assert that training a giant model on as much data as possible can eventually solve most language tasks, perhaps even leading to AGI . However, frontier LLMs still fall short on complex problems in long-tail domains. Errors occur somewhere in the process of encoding the necessary knowledge, surfacing it for a specific prompt, and synthesizing it with other input data. In this talk, I will argue that specialization is the right approach to improve LLMs here; that is, modifying them through training or other means to improve their factuality and reasoning capabilities. First, I will show that specialization is necessary: inference-only approaches like chain-of-thought prompting are not sufficient. Second, I will present our fact-checking system MiniCheck, which is fine-tuned on specialized data to detect factual errors in LLM responses, leading to a better detector than frontier models like GPT -4. Finally, I will discuss how to specialize LLMs to be better at logical reasoning. I argue that we need (a) better fine-tuning methods which make targeted adjustments to model behavior; (b) improved inference capabilities, such as a differentiable theorem prover that can be plugged into a standard Transformer. These forms of specialization represent a path towards fundamentally new capabilities in factuality and reasoning beyond what can be achieved in current models.

Bio: Greg Durrett is an associate professor of Computer Science at UT Austin. He received his BS in Computer Science and Mathematics from MIT and his PhD in Computer Science from UC Berkeley, where he was advised by Dan Klein. His research is broadly in the areas of natural language processing and machine learning. Currently, his group’s focus is on techniques for reasoning about knowledge in text, verifying factuality of LLM generations, and building systems using LLMs as primitives. He is a 2023 Sloan Research Fellow and a recipient of a 2022 NSF CAREER award. He has co-organized the Workshop on Natural Language Reasoning and Structured Explanations at ACL 2023 and ACL 2024 , as well as workshops on low-resource NLP and NLP for programming. He has served in numerous roles for *CL conferences, including as a member of the NAACL Board since 2024.

This talk is part of the Language Technology Lab Seminars series.

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