🔍 Today's pick in Interpretability & Analysis of LMs: A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains by 
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🔍 Today's pick in Interpretability & Analysis of LMs: A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains by 
@alonjacovi
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alonjacovi
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@yonatanbitton
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yonatanbitton
 B. Bohnet J. Herzig 
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 B. Bohnet J. Herzig 
@orhonovic
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orhonovic
 M. Tseng M. Collins 
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 M. Tseng M. Collins 
@roeeaharoni
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roeeaharoni
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@mega
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mega
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This work introduces a new methodology for human verification of reasoning chains and adopts it to annotate a dataset of chain-of-thought reasoning chains produced by 3 LMs. The annotated dataset, REVEAL, can be used to benchmark automatic verifiers of reasoning in LMs.
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This work introduces a new methodology for human verification of reasoning chains and adopts it to annotate a dataset of chain-of-thought reasoning chains produced by 3 LMs. The annotated dataset, REVEAL, can be used to benchmark automatic verifiers of reasoning in LMs.
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In their analysis, the authors find that LM-produced CoTs generally contain faulty steps, often leading to incorrect automatic verification. In particular, CoT-generating LMs are found to produce non-attributable reasoning steps often, and reasoning verifiers generally struggle to verify logical correctness.
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In their analysis, the authors find that LM-produced CoTs generally contain faulty steps, often leading to incorrect automatic verification. In particular, CoT-generating LMs are found to produce non-attributable reasoning steps often, and reasoning verifiers generally struggle to verify logical correctness.
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📄 Paper: 
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📄 Paper: 
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for
  Verifiers of Reasoning Chains (2402.00559)
https://huggingface.co/papers/2402.00559
2402.00559
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https://huggingface.co/papers/2402.00559
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🔡 Dataset: 
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🔡 Dataset: 
https://huggingface.co/datasets/google/reveal
google/reveal
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https://huggingface.co/datasets/google/reveal
🔍 Today's pick in Interpretability & Analysis of LMs: A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains by @alonjacovi @yonatanbitton B. Bohnet J. Herzig @orhonovic M. Tseng M. Collins @roeeaharoni @mega

This work introduces a new methodology for human verification of reasoning chains and adopts it to annotate a dataset of chain-of-thought reasoning chains produced by 3 LMs. The annotated dataset, REVEAL, can be used to benchmark automatic verifiers of reasoning in LMs.

In their analysis, the authors find that LM-produced CoTs generally contain faulty steps, often leading to incorrect automatic verification. In particular, CoT-generating LMs are found to produce non-attributable reasoning steps often, and reasoning verifiers generally struggle to verify logical correctness.

📄 Paper: https://huggingface.co/papers/2402.00559
🔡 Dataset: https://huggingface.co/datasets/google/reveal
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s3nh roeeaharoni alonjacovi osanseviero clem manu
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Yonatan-Bitton