Past talk · AI/ML Seminar Series
Researching and Revising What Language Models Say, Using Language Models
PhD Student, Department of Computer Science, UC Irvine
- Date & time
- Monday, April 24, 2023 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
As the strengths of large language models (LLMs) have become prominent, so too have their weaknesses. A glaring weakness of LLMs is their penchant for generating false, biased, or misleading claims in a phenomena broadly referred to as hallucinations. Most LLMs also do not ground their generations to any source. To enable attribution while still preserving all the powerful advantages of LLMs, we propose RARR (Retrofit Attribution using Research and Revision), a system that automatically retrieves evidence to support the output of any LLM and then post-edits the output to fix any information that contradicts the retrieved evidence while preserving the original output as much as possible. When applied to several state-of-the-art LLMs, RARR significantly improves attribution.
About the speaker
Anthony Chen is a final-year doctoral student advised by Sameer Singh. He is broadly interested in how we can evaluate the limits of large language models and design efficient methods to address their deficiencies, with recent focus on attribution and hallucinations in large language models.