Summary of Are Your Llms Capable Of Stable Reasoning?, by Junnan Liu et al.
Are Your LLMs Capable of Stable Reasoning?
by Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang, Kuikun Liu, Songyang Gao, Wenwei Zhang, Songyang Zhang, Kai Chen
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 investigates the discrepancy between Large Language Models’ (LLMs) benchmark performances and real-world applications. The authors identify the gap as stemming from current evaluation protocols and metrics, which inadequately capture LLMs’ capabilities in complex reasoning tasks. To address this issue, they introduce G-Pass@k, a novel evaluation metric that assesses model performance across multiple sampling attempts, quantifying both peak performance potential and stability. Additionally, they present LiveMathBench, a dynamic benchmark comprising challenging mathematical problems designed to minimize data leakage risks during evaluation. The authors provide comprehensive insights into state-of-the-art LLMs’ maximum capabilities and operational consistency using G-Pass@k on LiveMathBench. Their findings reveal substantial room for improvement in LLMs’ realistic reasoning capabilities, highlighting the need for more robust evaluation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how Large Language Models (LLMs) can be better at solving complex problems. Right now, there’s a gap between how well they do on tests and how well they do in real life. The authors think this is because the way we test them isn’t very good. They introduce two new ways to test LLMs: G-Pass@k, which looks at their performance over many attempts, and LiveMathBench, a special set of math problems that tries to make it harder for data leaks during testing. By using these new tests on state-of-the-art LLMs, the authors found that there’s still a lot of room for improvement in how well they can solve real-world problems. |
Keywords
» Artificial intelligence » Stemming
