# Explanation Generation for Contradiction Reconciliation with LLMs

arXiv: https://arxiv.org/abs/2603.22735 · HTML: https://mlsanity.com/paper/2603.22735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 60 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://mlsanity.com/paper/2411.15124/) (2024)
- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts](https://mlsanity.com/paper/2305.13300/) (2023)
- [Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models](https://mlsanity.com/paper/2404.18796/) (2024)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [Self-Preference Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2410.21819/) (2024)
- [What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://mlsanity.com/paper/2010.03532/) (2020)
- [Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence](https://mlsanity.com/paper/2210.13701/) (2022)
- [Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement](https://mlsanity.com/paper/2402.11436/) (2024)
- [Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration](https://mlsanity.com/paper/2406.15951/) (2024)
- [From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery](https://mlsanity.com/paper/2505.13259/) (2025)
- [Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations](https://mlsanity.com/paper/2310.11207/) (2023)
- [BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information](https://mlsanity.com/paper/2306.07934/) (2023)
- [UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations](https://mlsanity.com/paper/2311.08469/) (2023)
- [A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI](https://mlsanity.com/paper/2412.13942/) (2024)
- [Entailed Between the Lines: Incorporating Implication into NLI](https://mlsanity.com/paper/2501.07719/) (2025)
- [Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models](https://mlsanity.com/paper/2411.14103/) (2024)
- [Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals](https://mlsanity.com/paper/2505.13972/) (2025)
- [Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection](https://mlsanity.com/paper/2505.20209/) (2025)
- [NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic](https://mlsanity.com/paper/2307.02849/) (2023)
- [Abductive Reasoning with Syllogistic Forms in Large Language Models](https://mlsanity.com/paper/2603.06428/) (2026)

## Abstract (arXiv)

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 generation, where models must generate explanations that effectively render contradictory statements compatible. We propose a novel method of reusing natural language inference (NLI) datasets, and introduce quality metrics that enable scalable automatic evaluation. Experiments with 13 LLMs in thinking and non-thinking modes reveal models' limited success in this task, with longer reasoning chains negatively associated with explanation success. As a proof of concept, we further adapt our introduced task and metrics for reinforcement-learning training, finding that trained models exhibit improved accuracy in non-thinking mode on two narrative reasoning benchmarks (True Detective and MuSR). Our findings highlight reconciliatory explanation generation as a challenge in LLM reasoning and motivate further work to investigate its potential for improving model capabilities.