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arXiv:2608.30413v1 Announce Type: new Abstract: Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of n...
arXiv:2608. 14161v1 Announce Type: new Abstract: LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in high-stakes applications.
arXiv:2606. 04751v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks.
The paper introduces a new task called reconciliatory explanation generation, where large language models (LLMs) must produce explanations that make contradictory statements compatible. It proposes reusing natural language inference datasets and new quality metrics for scalable evaluation. Experiments with 13 LLMs show limited success, especially when longer reasoning chains are involved, but reinforcement‑learning training improves performance on two narrative reasoning benchmarks.
The paper introduces VERITAS, a method that verbalises a language model’s internal computation during chain‑of‑thought (CoT) generation by projecting layer‑wise Jacobians to produce a token‑by‑token account of reasoning evolution. By training a classifier on these verbalised features, VERITAS detects where and how reasoning diverges toward unfaithfulness, achieving a 28.4% balanced‑accuracy gain over LLM‑as‑Judge and a 16.0% improvement over activation probing across four datasets. The approach demonstrates generalisability to different datasets and model architectures, offering a richer, more interpretable lens on CoT faithfulness that moves beyond detection toward mechanistic insight.
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.