TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games
This provides a new benchmark for assessing LLMs' deductive reasoning in narrative contexts, which is incremental as it builds on existing evaluation methods.
The paper tackles the problem of evaluating deductive reasoning in Large Language Models by introducing TurnaboutLLM, a benchmark from detective games, and finds that it challenges state-of-the-art models, revealing limitations in strategies like Chain-of-Thought prompting.
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, the number of reasoning step and answer space size on model performance. Overall, TurnaboutLLM presents a substantial challenge for LLMs' deductive reasoning abilities in complex, narrative-rich environments.