LongDPO: Enhancing Long-Form Text Generation with Critique-Guided Stepwise Processing

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Improved Long Text Generation with LongDPO: Critique-Augmented, Stepwise Information Processing
The generation of long texts is crucial for various applications, from scientific papers to code generation. Despite considerable progress in the development of large language models (LLMs), the results in the area of long text generation often fall short of expectations. Problems such as deviations from the desired length and qualitative deficiencies frequently occur. A new approach called LongDPO promises a remedy by improving the generation of long texts through critique-augmented, stepwise information processing.
Challenges in Long Text Generation
Current methods that employ preference learning with outcome monitoring often don't provide detailed feedback for longer contexts. This leads to generated texts not fully meeting the requirements of the request. The desired length is not adhered to, coherence suffers, and the quality of the content leaves something to be desired. Especially with complex tasks that require a deeper understanding and stringent reasoning, previous models reach their limits.
LongDPO: A New Approach
LongDPO pursues an innovative approach by prioritizing process monitoring. Instead of only evaluating the final result, the generation process is analyzed and optimized step by step. Monte Carlo tree search is used to incrementally collect preference pairs. A global memory pool ensures the consistency of the generated content. To address the problem of suboptimal candidate selection, LongDPO integrates external critiques that refine the quality of the preference pairs.
Key Components of LongDPO
The core components of LongDPO are stepwise preference modeling and the integration of external critiques. Stepwise preference modeling allows the model to learn from the feedback gathered during the generation process and adjust its strategy. The integration of external critiques provides additional information that helps the model improve the quality of the generated texts. By combining these two components, LongDPO can overcome the weaknesses of existing methods and elevate long text generation to a new level.
Experimental Results
Initial experimental results show that LongDPO improves the length and quality of generated long texts in various benchmarks. Remarkably, these improvements are achieved without significant performance losses in general benchmarks and various model backbones. This suggests that LongDPO is a promising approach to improving long text generation and has the potential to advance the development of LLMs in this area.
Outlook
LongDPO represents an important step towards more robust and efficient long text generation. The integration of process monitoring and critique mechanisms opens new possibilities for the development of future LLMs. Further research is necessary to fully exploit the potential of this approach and push the boundaries of long text generation further. In particular, the scalability of the procedure and its application to various domains are promising research directions.
Bibliography: - Ping, B., Zeng, J., Meng, F., Wang, S., Zhou, J., & Zhang, S. (2025). LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information. *arXiv preprint arXiv:2502.02095*. - Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2024). Efficient Long Sequence Modeling via State Space Augmented Transformer. *arXiv preprint arXiv:2406.15319*. - Liu, J., Li, L., Lin, J., Dhingra, B., & Du, J. (2024). LONG2RAG: Evaluating Long-Context Long-Form Retrieval-Augmented Generation with Key Point Recall. *arXiv preprint arXiv:2410.23000*. - Shaham, U., Dalvi, B., Dagan, A., & Caciularu, A. (2023). Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor. *OpenReview*.