KNOT-MCTS: An Effective Approach to Addressing Hallucinations in Generative Language Modeling for Question Answering

Contemporary large language models (LLMs) have made significant advancements, capable of generating fluent conversations with humans and accomplishing various tasks such as programming and question answering (QA). Nevertheless, current LLMs are still faced with numerous challenges, including generating hallucinations, lacking the latest information, suffering from biases, and others. In this paper, we proposed a technique, Knowledge-based Navigation for Optimal Truthfulness Monte Carlo Tree Search (KNOT-MCTS), which can reduce hallucinations of LLMs by aligning semantics of responses with external knowledge during the generation process. This technique acts as a plug-and-play knowledge injection method, which does not require any training and can be applied to any (large) language model. First, we retrieve relevance knowledge snippets, incorporating them into the prompt section and subsequently fed into the decoding process. Then, during the decoding process, we utilize our semantic alignment heuristic function to guide the response generation process of LMs through the Monte Carlo Tree Search (MCTS) decoding process. In our experiments on the TruthfulQA dataset, KNOT-MCTS paired with various LMs consistently outperforms their respective baselines. Our results demonstrate that KNOT-MCTS can effectively inject knowledge into various LMs to reduce hallucinations of LMs.