Controlled Diversity: Length-optimized Natural Language Generation

• URL: http://arxiv.org/abs/2502.19347v1
• Date: Wed, 26 Feb 2025 17:38:58 GMT
• Title: Controlled Diversity: Length-optimized Natural Language Generation
• Authors: Diana Marie Schenke, Timo Baumann,
• Abstract summary: LLMs are not generally able to adjust the length of their outputs based on strict length requirements.<n>We present an approach to train LLMs to acquire this capability by augmenting existing data and applying existing fine-tuning techniques.<n>Our results indicate that our method may change the response quality when using training data that was not generated by the baseline model.
• Score: 1.3888744377495608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLMs are not generally able to adjust the length of their outputs based on strict length requirements, a capability that would improve their usefulness in applications that require adherence to diverse user and system requirements. We present an approach to train LLMs to acquire this capability by augmenting existing data and applying existing fine-tuning techniques, which we compare based on the trained models' adherence to the length requirement and overall response quality relative to the baseline model. Our results demonstrate that these techniques can be successfully applied to train LLMs to adhere to length requirements, with the trained models generating texts which better align to the length requirements. Our results indicate that our method may change the response quality when using training data that was not generated by the baseline model. This allows simultaneous alignment to another training objective in certain scenarios, but is undesirable otherwise. Training on a dataset containing the model's own responses eliminates this issue.

Related papers

• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications. Ensuring their alignment with the diverse preferences of individual users has become a critical challenge. We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• Language Models can Self-Lengthen to Generate Long Texts [74.96074422345806]
  This paper introduces an innovative iterative training framework called Self-Lengthen. It leverages only the intrinsic knowledge and skills of Large Language Models without the need for auxiliary data or proprietary models. Experiments on benchmarks and human evaluations show that Self-Lengthen outperforms existing methods in long-text generation.
  arXiv  Detail & Related papers  (2024-10-31T13:47:10Z)
• Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation [12.736045604858738]
  Recent advances in large language model (LLM) training have highlighted the need for diverse, high-quality instruction data.<n>We propose a paradigm shift named textbfNOMAD by investigating how to specifically train models for data generation.
  arXiv  Detail & Related papers  (2024-10-27T07:38:39Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models [14.175953642749649]
  Large language models often struggle to generate responses of a specific length. We introduce a novel, model-agnostic approach called Ruler to enhance the instruction-following ability of large language models under length-constrained instructions.
  arXiv  Detail & Related papers  (2024-09-27T17:44:58Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.