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This work investigates how to improve large language model (LLM)-based reasoning for knowledge base question answering (KBQA) via Monte Carlo Tree Search (MCTS).
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Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
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SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses
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The Value of Semantic Parse Labeling for Knowledge Base Question Answering. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , Katrin Erk and Noah A. Smith (Eds.). Association for Computational Linguistics, Berlin, Germany, 201–206
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