Length Controlled Generation for Black-box LLMs

• URL: http://arxiv.org/abs/2412.14656v1
• Date: Thu, 19 Dec 2024 09:07:38 GMT
• Title: Length Controlled Generation for Black-box LLMs
• Authors: Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong, Kun Zhu, Lei Huang, Tat-Seng Chua, Bing Qin,
• Abstract summary: Large language models (LLMs) have demonstrated impressive instruction following capabilities, but struggle to accurately manage the length of generated text. We propose a novel iterative sampling framework for text length control, integrating the Metropolis-Hastings algorithm with an importance sampling acceleration strategy. Our framework achieves almost 100% success rates of length control on Llama3.1 for tasks such as length-controlled abstractive summarization.
• Score: 70.57649832433451
• License:
• Abstract: Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is a fundamental requirement in many real-world applications. Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use. In this paper, we propose a novel iterative sampling framework for text length control, integrating the Metropolis-Hastings algorithm with an importance sampling acceleration strategy. This framework efficiently and reliably regulates LLMs to generate length-constrained text without modifying the underlying parameters, thereby preserving the original capabilities of LLMs. Experimental results demonstrate that our framework achieves almost 100\% success rates of length control on Llama3.1 for tasks such as length-controlled abstractive summarization and length-constrained instruction following, with minimal additional computational overhead. This also highlights the significant potential of our method for precise length control across a broader range of applications, without compromising the versatility of LLMs.

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