Summary of From Calculation to Adjudication: Examining Llm Judges on Mathematical Reasoning Tasks, by Andreas Stephan et al.
From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning Tasks
by Andreas Stephan, Dawei Zhu, Matthias Aßenmacher, Xiaoyu Shen, Benjamin Roth
First submitted to arxiv on: 6 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 investigates the performance of large language models (LLMs) as judges for mathematical reasoning tasks, which require multi-step reasoning and verifiable correctness. Unlike previous studies that evaluated LLM judges on generation tasks like summarization or machine translation, this study focuses on their ability to evaluate candidate models’ quality. The results show that most LLM judges fail to improve task performance but can identify the better model. A strong correlation is found between judgment performance and candidate model task performance. Furthermore, the analysis reveals that judges tend to choose the higher-quality model even if its answer is incorrect. Statistics-based approaches are proposed to predict judgment performance. The study also explores how LLM judges incorporate writing style in their judgments by swapping or masking candidate answers. This research contributes to a better understanding of LLM judges’ capabilities and potential limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models can judge the quality of other math problems they solve. Unlike previous studies that tested these models on simple tasks like summarizing text, this study challenges them with more complex math problems. The results show that most of these models aren’t good at improving the answers they give, but they can pick which model is better. It also finds a strong connection between how well a model judges and how well it solves math problems itself. This research helps us understand what these models are capable of and where they might struggle. |
Keywords
» Artificial intelligence » Summarization » Translation
