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[1]
A Pragmatic Way to Measure Chain-of-Thought Monitorability
2025Scott Emmons, Roland S. Zimmermann et al.
[2]
FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning
2025Xu Shen, Song Wang et al.
[3]
Thought Anchors: Which LLM Reasoning Steps Matter?
2025Paul C. Bogdan, Uzay Macar et al.
[4]
PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models
2025Mingyang Song, Zhao-yu Su et al.
[5]
ProcessBench: Identifying Process Errors in Mathematical Reasoning
2024Chujie Zheng, Zhenru Zhang et al.
[6]
A Survey on LLM-as-a-Judge
2024Jiawei Gu, Xuhui Jiang et al.
[7]
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
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[8]
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
2023Lianmin Zheng, Wei-Lin Chiang et al.
[9]
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
2023Yidong Wang, Zhuohao Yu et al.
[10]
Let's Verify Step by Step
2023H. Lightman, Vineet Kosaraju et al.
[11]
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
2023Sewon Min, Kalpesh Krishna et al.
[12]
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
2023Miles Turpin, Julian Michael et al.
[13]
Solving math word problems with process- and outcome-based feedback
2022Jonathan Uesato, Nate Kushman et al.
[14]
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
2022Abulhair Saparov, He He
[15]
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"A benchmark tool, C2-Faith, helps evaluate LLM judges on causal and coverage faithfulness in chain-of-thought reasoning."

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