# Prompt-Based Length Controlled Generation with Multiple Control Types

arXiv: https://arxiv.org/abs/2406.10278 · HTML: https://mlsanity.com/paper/2406.10278/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 44 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 30 of 32)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [TinyLlama: An Open-Source Small Language Model](https://mlsanity.com/paper/2401.02385/) (2024)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [Recursively Summarizing Books with Human Feedback](https://mlsanity.com/paper/2109.10862/) (2021)
- [Deep Reinforcement Learning for Dialogue Generation](https://mlsanity.com/paper/1606.01541/) (2016)
- [Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization](https://mlsanity.com/paper/2210.01241/) (2022)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://mlsanity.com/paper/1907.12412/) (2019)
- [An Actor-Critic Algorithm for Sequence Prediction](https://mlsanity.com/paper/1607.07086/) (2016)
- [Human-centric Dialog Training via Offline Reinforcement Learning](https://mlsanity.com/paper/2010.05848/) (2020)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [Text Generation by Learning from Demonstrations](https://mlsanity.com/paper/2009.07839/) (2020)
- [Positional Encoding to Control Output Sequence Length](https://mlsanity.com/paper/1904.07418/) (2019)
- [Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus](https://mlsanity.com/paper/2002.00748/) (2020)
- [Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints](https://mlsanity.com/paper/1603.08887/) (2016)
- [Controlling Dialogue Generation with Semantic Exemplars](https://mlsanity.com/paper/2008.09075/) (2020)
- [End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient](https://mlsanity.com/paper/1712.02838/) (2017)

## Built on by (most cited first, 19 of 19)

- [Zero-Shot Strategies for Length-Controllable Summarization](https://mlsanity.com/paper/2501.00233/) (2024)
- [CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards](https://mlsanity.com/paper/2512.23971/) (2025)
- [Can LLMs Track Their Output Length? A Dynamic Feedback Mechanism for Precise Length Regulation](https://mlsanity.com/paper/2601.01768/) (2026)
- [Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs](https://mlsanity.com/paper/2508.13805/) (2025)
- [Steering Language Models Before They Speak: Logit-Level Interventions](https://mlsanity.com/paper/2601.10960/) (2026)
- [Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization](https://mlsanity.com/paper/2410.18624/) (2024)
- [C3TG: Conflict-aware, Composite, and Collaborative Controlled Text Generation](https://mlsanity.com/paper/2511.09292/) (2025)
- [Self-controller: Controlling LLMs with Multi-round Step-by-step Self-awareness](https://mlsanity.com/paper/2410.00359/) (2024)
- [Considering Length Diversity in Retrieval-Augmented Summarization](https://mlsanity.com/paper/2503.09249/) (2025)
- [Strategic Planning and Rationalizing on Trees Make LLMs Better Debaters](https://mlsanity.com/paper/2505.14886/) (2025)
- [Controlling Distributional Bias in Multi-Round LLM Generation via KL-Optimized Fine-Tuning](https://mlsanity.com/paper/2604.05756/) (2026)
- [Measuring temporal effects of agent knowledge by date-controlled tool use](https://mlsanity.com/paper/2503.04188/) (2025)
- [Length Representations in Large Language Models](https://mlsanity.com/paper/2507.20398/) (2025)
- [LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models](https://mlsanity.com/paper/2603.19255/) (2026)
- [VisuCraft: Enhancing Large Vision-Language Models for Complex Visual-Guided Creative Content Generation via Structured Information Extraction](https://mlsanity.com/paper/2508.02890/) (2025)
- [BeLLMan: Controlling LLM Congestion](https://mlsanity.com/paper/2510.15330/) (2025)
- [Progress Ratio Embeddings: An Impatience Signal for Robust Length Control in Neural Text Generation](https://mlsanity.com/paper/2512.06938/) (2025)
- [From Instruction to Output: The Role of Prompting in Modern NLG](https://mlsanity.com/paper/2602.11179/) (2026)
- [ScopeIF: Improving Scope-Aware Precise Instruction-Following in Large Language Models via Graded Reward Modeling](https://mlsanity.com/paper/2609.32189/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have attracted great attention given their strong performance on a wide range of NLP tasks. In practice, users often expect generated texts to fall within a specific length range, making length controlled generation an important topic, especially for GPT-style models. Existing length control methods mostly focus on a simple control type of "equal to" a target length. Different from them, we propose a prompt-based method to achieve length controlled generation under different control types with high accuracy. In particular, we adopt reinforcement learning (RL) and sample filtering with the reward signal given by rule-based reward models, which enhances the length control ability of models by rewarding outputs that follow certain control instructions. In addition, we introduce a standard prompt extractor to parse arbitrary users' input into standard control instructions. Experiments show that our method significantly improves the accuracy of prompt-based length control on popular summarization datasets like CNNDM and NYT under multiple control types. Moreover, both the standard prompt extractor and RL-tuned model show strong generalization to unseen control prompt templates.