Papers with length control methods

2 papers
Prompt-Based Length Controlled Generation with Multiple Control Types (2024.findings-acl)

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Challenge: Existing length control methods focus on a simple control type of “equal to” a target length.
Approach: They propose a prompt-based method to achieve length controlled generation under different control types with high accuracy by using reinforcement learning and sample filtering with the reward signal given by rule-based reward models.
Outcome: The proposed method significantly improves the accuracy of prompt-based length control on popular summarization datasets like CNNDM and NYT under multiple control types.
Length Controlled Generation for Black-box LLMs (2025.acl-long)

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Challenge: Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use.
Approach: They propose an iterative sampling framework that regulates LLMs to generate length-constrained text without modifying the underlying parameters.
Outcome: The proposed method achieves 100% success rates on Llama3.1 tasks with minimal additional computational overhead.

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