Summary of Weak-eval-strong: Evaluating and Eliciting Lateral Thinking Of Llms with Situation Puzzles, by Qi Chen et al.
Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles
by Qi Chen, Bowen Zhang, Gang Wang, Qi Wu
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Medium Difficulty Summary: The paper introduces SPLAT, a benchmark to evaluate Large Language Models’ (LLMs) lateral thinking capabilities. LLMs excel in vertical thinking tasks but struggle with creative thought processes due to the scarcity of relevant data and complexity in assessing these processes. SPLAT leverages situation puzzles across three difficulty levels to elicit LAteral Thinking of LLMs through a multi-turn player-judge framework. This approach simulates an interactive game, reducing dependence on robust evaluation models. Experiments demonstrate that WizardLM-2 closely matches human judgements in intermediate question-answering and final scenario accuracy, achieving over 80% agreement. The paper also applies the SPLAT data to other lateral thinking-related benchmarks, enhancing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: A new way is being developed to test how well computers can think creatively. This is a hard problem because it’s not easy to tell if a computer is really using its imagination or just doing what it was programmed to do. The researchers created a set of puzzles that get progressively harder, and they used these puzzles to test how well some powerful language models could solve them. They found that one model, called WizardLM-2, did surprisingly well when compared to humans. This is important because it shows that we can create better computer programs if we design them to think more like people. |
Keywords
» Artificial intelligence » Question answering
