Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering
This addresses a critical issue for users relying on LLMs for tasks like fact-checking and question answering, but it appears incremental as it builds on existing disambiguation strategies.
The study tackled the problem of ambiguity in natural language for Large Language Models (LLMs) in open-domain question answering, showing that simple, training-free disambiguation methods can improve LLM performance, though specific numerical gains are not provided.
Ambiguity in natural language poses significant challenges to Large Language Models (LLMs) used for open-domain question answering. LLMs often struggle with the inherent uncertainties of human communication, leading to misinterpretations, miscommunications, hallucinations, and biased responses. This significantly weakens their ability to be used for tasks like fact-checking, question answering, feature extraction, and sentiment analysis. Using open-domain question answering as a test case, we compare off-the-shelf and few-shot LLM performance, focusing on measuring the impact of explicit disambiguation strategies. We demonstrate how simple, training-free, token-level disambiguation methods may be effectively used to improve LLM performance for ambiguous question answering tasks. We empirically show our findings and discuss best practices and broader impacts regarding ambiguity in LLMs.