Retrieval-Augmented Thought Process as Sequential Decision Making

• URL: http://arxiv.org/abs/2402.07812v1
• Date: Mon, 12 Feb 2024 17:17:50 GMT
• Title: Retrieval-Augmented Thought Process as Sequential Decision Making
• Authors: Thomas Pouplin, Hao Sun, Samuel Holt, Mihaela van der Schaar
• Abstract summary: We introduce the Retrieval-Augmented Thought Process (RATP) RATP formulates the thought generation of Large Language Models as a multiple-step decision process. It achieves a 50% improvement over existing in-context retrieval-augmented language models.
• Score: 58.87539195379386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated their strong ability to assist people and show "sparks of intelligence". However, several open challenges hinder their wider application: such as concerns over privacy, tendencies to produce hallucinations, and difficulties in handling long contexts. In this work, we address those challenges by introducing the Retrieval-Augmented Thought Process (RATP). Given access to external knowledge, RATP formulates the thought generation of LLMs as a multiple-step decision process. To optimize such a thought process, RATP leverages Monte-Carlo Tree Search, and learns a Q-value estimator that permits cost-efficient inference. In addressing the task of question-answering with private data, where ethical and security concerns limit LLM training methods, RATP achieves a 50% improvement over existing in-context retrieval-augmented language models.

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