Explanation Generation for Contradiction Reconciliation with LLMs
Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of human reasoning in social and professional settings is the ability to hypothesize explanations that reconcile contradictions. For example, "Cassie hates coffee" and "She buys coffee everyday" appear contradictory, but are in fact compatible if Cassie was assigned the daily chore of buying coffee for all her coworkers. Despite advances in the reasoning capabilities of large language models (LLMs), their ability to hypothesize such explanations remains largely unexplored. To address this gap, we introduce the task of reconciliatory explanation gene...
| Authors | Jason Chan, Zhixue Zhao, Robert Gaizauskas |
|---|---|
| Primary category | cs.CL |
| Published | 24 March 2026 |
| Last updated at source | 15 October 2026 |
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