Refining LLMs with Reinforcement Learning for Human-Like Text Generation

Abstract

Large Language Models (LLMs) are used widely for tasks involving text generation such as dialogue summarization and creative writing. The generated text often appears unnatural, and this text can easily be distinguished from natural language. In this paper, we leverage the capabilities of Reinforcement Learning to fine-tune LLMs so as to produce text that resembles human language. We have applied the Proximal Policy Optimization algorithm to fine tune a FLAN-T5 LLM for a dialogue summarization task. © 2024 IEEE.