Prompt-Based Length Controlled Generation with Reinforcement Learning

• URL: http://arxiv.org/abs/2308.12030v2
• Date: Sat, 30 Sep 2023 07:54:22 GMT
• Title: Prompt-Based Length Controlled Generation with Reinforcement Learning
• Authors: Renlong Jie, Xiaojun Meng, Lifeng Shang, Xin Jiang, Qun Liu
• Abstract summary: We propose a prompt-based length control method to achieve high-accuracy length controlled generation. We adopt reinforcement learning with the reward signal given by either trainable or rule-based reward models. Our method significantly improves the accuracy of prompt-based length control for summarization task on popular datasets like CNNDM and NYT.
• Score: 48.49553921757085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) like ChatGPT and GPT-4 have attracted great attention given their surprising performance on a wide range of NLP tasks. Length controlled generation of LLMs emerges as an important topic, which enables users to fully leverage the capability of LLMs in more real-world scenarios like generating a proper answer or essay of a desired length. In addition, the autoregressive generation in LLMs is extremely time-consuming, while the ability of controlling this generated length can reduce the inference cost by limiting the length. Therefore, we propose a prompt-based length control method to achieve high-accuracy length controlled generation. In particular, we adopt reinforcement learning with the reward signal given by either trainable or rule-based reward models, which further enhances the length-control ability of LLMs by rewarding outputs that follows pre-defined control instruction. To enable rule-based inference, we also introduce standard prompt extractor to collect the standard control information from users' input. Experiments show that our method significantly improves the accuracy of prompt-based length control for summarization task on popular datasets like CNNDM and NYT. Both the standard prompt extractor and the RL-tuned model have show strong generalization ability to unseen control prompt templates.

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