arXiv Computation and Language
Oct 16

Explanation Generation for Contradiction Reconciliation with LLMs

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.

By Jason Chan, Zhixue Zhao, Robert Gaizauskas
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.

By Mila Gorecki, Moritz Hardt