MCTS-KBQA: Monte Carlo Tree Search with Information Gain Rewards for Knowledge Base Question Answering

Excerpt

arXiv:2502.13428v2 Announce Type: replace Abstract: This work investigates how to improve large language model (LLM)-based reasoning for knowledge base question answering (KBQA) via Monte Carlo Tree Search (MCTS). Applying MCTS to LLM-based KBQA remains challenging because reward design is difficult and rollout-based search is computationally expensive. Existing MCTS-style methods either rely on direct LLM scoring or require substantial data to train separate reward models, and they often provid