Counter-Hypothesis Generation: Towards Evaluating How LLMs Reason about Alternatives

Abstract

Reasoning about alternatives is a fundamental component of human cognition and argumentation, yet it remains unclear whether large language models (LLMs) can coherently generate and assess them. This paper introduces Counter-Hypothesis Generation (CHG), a novel task for evaluating how LLMs construct plausible hypotheses when contextual information changes. Inspired by open-domain commonsense reasoning, where models infer and compare multiple explanations, CHG bridges commonsense and counterfactual reasoning by requiring models to generate hypotheses that remain logically consistent with modified premises. We present a test set annotated by a human expert and complemented with counter-hypotheses generated by OpenAI-o3 and DeepSeek-r1. Experimental results reveal that even advanced reasoning models exhibit notable limitations in counter-hypothesis generation.

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