TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games

ACL ARR 2025 May Submission953 Authors

Abstract: This paper introduces TurnaboutLLM, a novel framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gameplay of detective games Ace Attorney and Danganronpa. The framework tasks LLMs with identifying contradictions between testimonies and evidences within long narrative contexts, a challenging task due to the large answer space and diverse reasoning types presented by its questions. We evaluate twelve state-of-the-art LLMs on the dataset, hinting at limitations of popular strategies for enhancing deductive reasoning such as extensive thinking and Chain-of-Thought prompting. The results also suggest varying effects of context size, reasoning steps and answer space size on model performance. Overall, TurnaboutLLM presents a substantial challenge for LLMs' deductive reasoning abilities in complex, narrative-rich environments.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: benchmarking, NLP datasets, automatic evaluation of datasets, evaluation

Contribution Types: Model analysis & interpretability

Languages Studied: English, Chinese, Japanese

Submission Number: 953