Summary of Step-by-step Reasoning to Solve Grid Puzzles: Where Do Llms Falter?, by Nemika Tyagi et al.
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?
by Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin RRV, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral
First submitted to arxiv on: 20 Jul 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 Solving grid puzzles involves a significant amount of logical reasoning, making it an ideal domain to evaluate the reasoning capability of language models (LLMs). Most existing works focus solely on the final predicted answer, neglecting in-depth analysis of the LLMs’ reasoning chains or providing finer metrics. To accurately evaluate the reasoning abilities of LLMs, we propose a new error taxonomy derived from manual analysis of reasoning chains from GPT-4, Claude-3, Gemini, Mistral, and Llama-2. We also develop GridPuzzle, an evaluation dataset comprising 274 grid-based puzzles with different complexities, as well as an LLM-based framework for large-scale subjective evaluation (identifying errors) and the objective metric PuzzleEval to evaluate correctness of reasoning chains. Our findings highlight the importance of understanding fine-grained errors and present a challenge for future research to enhance LLMs’ puzzle-solving abilities by developing methods that address these errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Grid puzzles require logical reasoning, making it a great way to test language models (LLMs). Most people only look at whether the model gets the final answer right, but we need to see how they got there. To do this, we created GridPuzzle, a big dataset of 274 grid-based puzzles with different levels of difficulty. We also developed a new way to measure how well LLMs reason and made an error taxonomy that shows where they go wrong. This helped us find some interesting things about how LLMs think. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Llama
