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Does Reasoning Make Language Models Agree?
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arXiv Machine Learning
1d ago
Does Reasoning Make Language Models Agree?
The paper investigates how reasoning affects agreement among language models. It distinguishes reasoning as an intervention from reasoning as an observed behavior, comparing agreement under controlled changes in reasoning effort versus observed reasoning lengths. Across nine models and three classification tasks, enabling reasoning increases agreement on the same individuals, while longer reasoning traces correlate with less consistent predictions across models.
arXiv AI
Jun 2
An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models
arXiv:2606. 01462v1 Announce Type: new Abstract: Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch.
arXiv Machine Learning
Sep 10
Do Reasoning Representations Help Humans Evaluate LLM Outputs?
The paper investigates whether reasoning representations—explanations for large language model outputs—aid humans in evaluating those outputs. A controlled human study tested six reasoning formats across tasks of varying complexity, measuring structural understanding, error detection, and trust calibration. Results revealed a mismatch: participants favored planning- and decomposition-based representations, yet simpler chain-of-thought traces better supported verification, trust, and interpretability, while preferred formats increased calibration risks.
arXiv AI
Jul 24
Post-Hoc Reasoning in Chain of Thought: Decoding and Steering Pre-Committed Answers
arXiv:2603. 01437v2 Announce Type: replace Abstract: As chain of thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a promising tool for interpretability, suggesting the opportunity to understand model decisions through verbalized reasoning.
arXiv AI
Jun 11
Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models
arXiv:2606. 11211v1 Announce Type: cross Abstract: The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment.
arXiv AI
Aug 17
Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models
arXiv:2608. 13760v1 Announce Type: cross Abstract: Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors?