Summary of Why Does In-context Learning Fail Sometimes? Evaluating In-context Learning on Open and Closed Questions, by Xiang Li et al.
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
by Xiang Li, Haoran Tang, Siyu Chen, Ziwei Wang, Ryan Chen, Marcin Abram
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 The authors investigate how well large-language models perform when given a relevant context for open and closed scientific questions. They created a unique benchmark with 100 pairs of questions and contexts, varying in relevance to the topic. Surprisingly, they find that a highly relevant context doesn’t always lead to better performance. This is particularly true for open-ended questions or those that are difficult or new. The results highlight a difference in how models handle close-form versus open-form questions and emphasize the need for more comprehensive evaluation of in-context learning on various question types. Additionally, the study raises the question of optimally selecting contexts for large language models, such as in Retrieval Augmented Generation (RAG) systems. The answer may depend on factors like question format, difficulty level, and novelty or popularity of the sought information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at answering scientific questions! Researchers created a special test to see how well these models do when given extra context about what they’re looking for. They found that giving the model more relevant info doesn’t always make it smarter. In fact, sometimes it even makes things worse! This is especially true when asking open-ended or tricky questions. The study shows that there’s a big difference in how these models handle easy and hard questions, and we need to be careful about how we evaluate their performance. It also raises an important question: what kind of context should we give these models to help them answer our questions best? The answer might depend on things like the type of question, how hard it is, or how new the information is. |
Keywords
* Artificial intelligence * Rag * Retrieval augmented generation
