Summary of Logicbench: Towards Systematic Evaluation Of Logical Reasoning Ability Of Large Language Models, by Mihir Parmar et al.
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
by Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral
First submitted to arxiv on: 23 Apr 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 The paper presents a comprehensive evaluation of the logical reasoning ability of large language models (LLMs) on 25 different reasoning patterns spanning propositional, first-order, and non-monotonic logics. To facilitate systematic evaluation, the authors introduce LogicBench, a natural language question-answering dataset focusing on the use of a single inference rule. The study uses chain-of-thought prompting with various LLMs, including GPT-4, ChatGPT, Gemini, Llama-2, and Mistral. The results show that existing LLMs struggle with complex reasoning and negations, often overlooking contextual information necessary for correct conclusions. This work aims to facilitate future research on evaluating and enhancing the logical reasoning ability of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well large language models can reason about things. These models are good at understanding language, but we don’t know if they can really think logically. The authors tested 25 different ways that these models could reason using a special dataset called LogicBench. They used five different models to see how well they did. The results showed that the models aren’t very good at reasoning about complex things and often forget important details. |
Keywords
» Artificial intelligence » Gemini » Gpt » Inference » Llama » Prompting » Question answering
