PathFinder: A Novel Approach for Multi-Hop Question Answering Using LLM Feedback and MCTS

This research explores a new method for improving multi-hop question answering by combining Monte Carlo Tree Search (MCTS) with feedback from a Large Language Model (LLM). The paper likely demonstrates a potentially significant advancement in the field by leveraging the strengths of both search and language modeling.

• •The core innovation lies in the integration of MCTS for path selection.
• •The system uses LLM feedback to guide the search process of MCTS, improving accuracy.
• •This approach targets improving multi-hop question answering performance.