Summary of Do Llms Understand Ambiguity in Text? a Case Study in Open-world Question Answering, by Aryan Keluskar et al.
Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering
by Aryan Keluskar, Amrita Bhattacharjee, Huan Liu
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) are struggling with ambiguity in natural language, leading to misinterpretations, miscommunications, hallucinations, and biased responses. This weakness hampers their ability to perform tasks like fact-checking, question answering, feature extraction, and sentiment analysis. Our study focuses on open-domain question answering as a test case, comparing off-the-shelf and few-shot LLM performance. We demonstrate that simple, training-free token-level disambiguation methods can effectively improve LLM performance for ambiguous question answering tasks. Our findings highlight the importance of explicit disambiguation strategies in improving LLMs’ ability to handle ambiguity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are special kinds of artificial intelligence that help us understand language better. But, they’re not perfect and often struggle with understanding words or phrases that have different meanings. This can lead to mistakes and inaccuracies in their responses. In this study, we tested how well LLMs do when faced with ambiguous questions. We found that by using a simple technique to clarify the meaning of individual words, we can improve the accuracy of LLMs’ answers. Our results show that explicit disambiguation strategies are important for improving LLM performance and ensuring they provide accurate responses. |
Keywords
» Artificial intelligence » Feature extraction » Few shot » Question answering » Token
