Enabling Synergistic Knowledge Sharing and Reasoning in Large Language Models with Collaborative Multi-Agents
Abstract
Despite the significant advancements in the field of Natural Language Processing (NLP), Large Language Models (LLMs) have shown limitations in performing complex tasks that require arithmetic, commonsense, and symbolic reasoning. Reasoning frameworks like ReAct, Chain-of-thought (CoT), Tree-of-thoughts (ToT), etc. have shown success but with limitations in solving long-form complex tasks.
Year of Publication
2023
Date Published
11
Publisher
IEEE
ISBN Number
979-8-3503-3912-3