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Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation

Created by
  • Haebom

Author

Seganrasan Subramanian, Abhigya Verma

Outline

This paper proposes a synthetic long-text context data generation framework to enhance the ability of large-scale language models (LLMs) to process and infer long-text inputs. To address the lack of high-quality, diverse, and verifiable long-text context datasets, we present a modular and extensible framework for generating data through prompt-based LLM interactions. This framework supports various learning and alignment objectives (SFT, DPO, and GRPO) and incorporates four data generation paradigms: multi-round conversations, document-based input-output pairs, verifiable command-response tasks, and long-text inference examples. Template-based prompting, a model-independent architecture, and metadata-rich output facilitate the generation of scalable, controllable, and purpose-specific datasets.

Takeaways, Limitations

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Takeaways:
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A novel framework that can contribute to solving the problem of lack of high-quality long-text context datasets is presented.
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Suggesting the possibility of improving LLM performance by supporting various learning and alignment objectives such as SFT, DPO, and GRPO.
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Modular and scalable architecture enables generation of various types of long-form context data.
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Improve efficiency and control over your data creation process with template-based prompting and metadata.
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Limitations:
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Lack of quantitative assessment of the quality and diversity of the data generated.
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May be heavily reliant on prompt engineering
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Lack of experimental verification of the proposed framework's effectiveness in improving actual LLM performance.
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Further research is needed to determine whether the findings are specific to a specific LLM and whether they can be generalized across different LLMs.
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