Despite their outstanding capabilities, large language models (LLMs) are prone to hallucination and producing factually incorrect information. This challenge has spurred efforts in attributed text generation, which prompts LLMs to generate content with supporting evidence. In this paper, we propose a novel framework, called Think&Cite, and formulate attributed text generation as a multi-step reasoning problem integrated with search. Specifically, we propose Self-Guided Monte Carlo Tree Search (SG-MCTS), which capitalizes on the self-reflection capability of LLMs to reflect on the intermediate states of MCTS for guiding the tree expansion process. To provide reliable and comprehensive feedback, we introduce Progress Reward Models to measure the progress of tree search from the root to the current state from two aspects, i.e., generation and attribution progress. We conduct extensive experiments on three datasets and the results show that our approach significantly outperforms baseline approaches.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. The first score of the Super Bowl turned out to be historic, but the record didn't stand for long. Kansas City Chiefs kicker Harrison Butker drilled his second field goal of Super Bowl LVIII against the San Francisco 49ers, a 57-yarder that rewrote the record for longest kick in the "Big Game." And that previous record was hit in the first half by 49ers rookie kicker Jake Moody, who hit one home from 56 yards.