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[2505.11824] Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Summary
This paper presents a novel method for identifying errors in stepwise reasoning using latent veracity inference, enhancing the reliability of language models in various reasoning tasks.
Why It Matters
As language models become integral in decision-making processes, ensuring their reasoning accuracy is crucial. This research addresses the challenge of inaccuracies in reasoning chains, proposing a method that improves model trustworthiness and performance across multiple reasoning benchmarks.
Key Takeaways
- Introduces latent veracity inference to enhance reasoning accuracy in language models.
- Presents Veracity Search (VS) for efficient error identification in reasoning chains.
- Demonstrates the effectiveness of the Amortized Veracity Inference (AVI) method in zero-shot contexts.
- Empirical results show improved performance on logical, mathematical, and commonsense reasoning tasks.
- Highlights the potential for self-correction and feedback mechanisms in AI systems.
Computer Science > Machine Learning arXiv:2505.11824 (cs) [Submitted on 17 May 2025 (v1), last revised 17 Feb 2026 (this version, v3)] Title:Latent Veracity Inference for Identifying Errors in Stepwise Reasoning Authors:Minsu Kim, Jean-Pierre Falet, Oliver E. Richardson, Xiaoyin Chen, Moksh Jain, Sungjin Ahn, Sungsoo Ahn, Yoshua Bengio View a PDF of the paper titled Latent Veracity Inference for Identifying Errors in Stepwise Reasoning, by Minsu Kim and 7 other authors View PDF HTML (experimental) Abstract:Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can contain inaccurate statements that reduce performance and trustworthiness. To address this, we propose to augment each reasoning step in a CoT with a latent veracity (or correctness) variable. To efficiently explore this expanded space, we introduce Veracity Search (VS), a discrete search algorithm over veracity assignments. It performs otherwise intractable inference in the posterior distribution over latent veracity values by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time verification method facilitates supervised fine-tuning of an Amortized Veracity Inference (AVI) machine by providing pseudo-labels for veracity. AVI generalizes VS, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that VS reliably identifies errors ...
