Computer Science > Computation and Language
[Submitted on 23 Aug 2023 (v1), last revised 30 Sep 2023 (this version, v2)]
Title:Prompt-Based Length Controlled Generation with Reinforcement Learning
View PDFAbstract: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.
Submission history
From: Renlong Jie [view email][v1] Wed, 23 Aug 2023 09:43:10 UTC (594 KB)
[v2] Sat, 30 Sep 2023 07:54:22 UTC (773 KB)
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