Summary of Do Large Language Models Have Problem-solving Capability Under Incomplete Information Scenarios?, by Yuyan Chen et al.
Do Large Language Models have Problem-Solving Capability under Incomplete Information Scenarios?
by Yuyan Chen, Tianhao Yu, Yueze Li, Songzhou Yan, Sijia Liu, Jiaqing Liang, Yanghua Xiao
First submitted to arxiv on: 23 Sep 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 This paper proposes a novel game, BrainKing, to evaluate the problem-solving capabilities of Large Language Models (LLMs) under incomplete information scenarios. The existing games, such as Twenty Questions and Who is undercover, have limitations in evaluating LLMs’ abilities to recognize misleading cues. BrainKing requires LLMs to identify target entities with limited yes-or-no questions and potential misleading answers, providing a more comprehensive assessment of their capabilities and limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new game called BrainKing to test how well Large Language Models (LLMs) can solve problems when they don’t have all the information. Right now, there are games like Twenty Questions that are not very good at testing this skill because they don’t involve recognizing misleading clues. The new game is inspired by another one called Who is undercover, but it’s more challenging and objective. By having three levels of difficulty, BrainKing can show how well LLMs do in different situations. |
