Summary of Generating Diverse Hypotheses For Inductive Reasoning, by Kang-il Lee et al.
Generating Diverse Hypotheses for Inductive Reasoning
by Kang-il Lee, Hyukhun Koh, Dongryeol Lee, Seunghyun Yoon, Minsung Kim, Kyomin Jung
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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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 This paper explores the capabilities of large language models (LLMs) in performing inductive reasoning, a fundamental aspect of human intelligence. Recent studies suggest that LLMs can engage in this process by sampling multiple hypotheses and selecting the best one explaining the observations. However, current methods suffer from significant wastage of compute due to the generation of semantically redundant hypotheses. The authors propose a novel method to improve diversity while maintaining text quality, building upon their analysis of the temperature parameter’s effect on LLMs’ hypotheses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Inductive reasoning is when we figure out general rules based on a few observations. Recent research showed that big language models can do this by trying out different ideas and choosing the best one. But right now, these models waste a lot of computer power because they come up with lots of extra, similar ideas. This paper looks at how to make these models work better without making them say silly things. |
Keywords
» Artificial intelligence » Temperature
