Summary of Mirage: Evaluating and Explaining Inductive Reasoning Process in Language Models, by Jiachun Li et al.
MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models
by Jiachun Li, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao
First submitted to arxiv on: 12 Oct 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 A novel dataset, MIRAGE, is introduced to comprehensively evaluate large language models’ (LLMs) inductive reasoning abilities. The study reveals that LLMs are poor rule-based reasoners, often relying on observation rather than a correct rule to make predictions. However, they excel at neighbor-based reasoning, leveraging similar examples to improve deductive performance. The authors demonstrate the limitations of current prompting methods and highlight the importance of considering input distribution, task scenario, and task difficulty when evaluating LLMs’ inductive capabilities. This research contributes to the development of more effective LLMs by understanding their strengths and weaknesses in inductive reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting smarter! Researchers created a special dataset called MIRAGE to test how well they can reason and make predictions based on rules and patterns. What did they find out? These models aren’t very good at following specific rules, but they’re great at looking at similar examples and making smart guesses. This is important because it helps us understand what these models are capable of doing and how we can make them even better. |
Keywords
» Artificial intelligence » Prompting
