# Zero-Shot Strategies for Length-Controllable Summarization

arXiv: https://arxiv.org/abs/2501.00233 · HTML: https://mlsanity.com/paper/2501.00233/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 31 Dec 2024.

## Builds on (most cited first, 20 of 20)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [Generating Sequences by Learning to Self-Correct](https://mlsanity.com/paper/2211.00053/) (2022)
- [Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization](https://mlsanity.com/paper/2302.08081/) (2023)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [Evaluating Large Language Models on Controlled Generation Tasks](https://mlsanity.com/paper/2310.14542/) (2023)
- [Controllable Text Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2408.12599/) (2024)
- [Following Length Constraints in Instructions](https://mlsanity.com/paper/2406.17744/) (2024)
- [Benchmarking Large Language Models on Controllable Generation under Diversified Instructions](https://mlsanity.com/paper/2401.00690/) (2024)
- [Prompt-Based Length Controlled Generation with Multiple Control Types](https://mlsanity.com/paper/2406.10278/) (2024)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [LenAtten: An Effective Length Controlling Unit For Text Summarization](https://mlsanity.com/paper/2106.00316/) (2021)
- [The Current State of Summarization](https://mlsanity.com/paper/2305.04853/) (2023)
- [Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?](https://mlsanity.com/paper/2401.10415/) (2024)
- [InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models](https://mlsanity.com/paper/2406.11097/) (2024)

## Built on by (most cited first, 10 of 10)

- [Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs](https://mlsanity.com/paper/2506.07240/) (2025)
- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs](https://mlsanity.com/paper/2508.13805/) (2025)
- [From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MARKERGEN](https://mlsanity.com/paper/2502.13544/) (2025)
- [Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search](https://mlsanity.com/paper/2509.26435/) (2025)
- [The AI Co-Ethnographer: How Far Can Automation Take Qualitative Research?](https://mlsanity.com/paper/2505.00012/) (2025)
- [TiCo: Time-Controllable Spoken Dialogue Model](https://mlsanity.com/paper/2603.22267/) (2026)
- [A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation](https://mlsanity.com/paper/2605.19316/) (2026)
- [Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition](https://mlsanity.com/paper/2607.14769/) (2026)
- [Towards Faithful Sentimental Image Captioning via Evidence-Aware Multi-Agent Reasoning](https://mlsanity.com/paper/2607.25789/) (2026)

## Abstract (arXiv)

Large language models (LLMs) struggle with precise length control, particularly in zero-shot settings. We conduct a comprehensive study evaluating LLMs' length control capabilities across multiple measures and propose practical methods to improve controllability. Our experiments with LLaMA 3 reveal stark differences in length adherence across measures and highlight inherent biases of the model. To address these challenges, we introduce a set of methods: length approximation, target adjustment, sample filtering, and automated revisions. By combining these methods, we demonstrate substantial improvements in length compliance while maintaining or enhancing summary quality, providing highly effective zero-shot strategies for precise length control without the need for model fine-tuning or architectural changes. With our work, we not only advance our understanding of LLM behavior in controlled text generation but also pave the way for more reliable and adaptable summarization systems in real-world applications.