用检索增强生成技术，自动生成科研论文的未来研究方向。
FutureGen: A RAG-based Approach to Generate the Future Work of Scientific Article
- 引入RAG框架，融合相关论文上下文提升建议全面性。
- GPT-4o mini搭配反馈机制，生成内容质量最优。
- 适合科研新手找课题或学者拓展合作方向。
科学论文的未来工作部分通过指出当前研究的局限与空白，为早期研究者提供未探索领域，也为资深研究者提供新项目或合作思路。本文旨在从科学论文中自动生成未来工作建议。为增强生成过程的广度并减少遗漏重要研究方向的风险，我们采用相关论文的上下文信息进行检索增强生成（RAG）。实验中测试了多种集成到RAG中的大语言模型（LLM），并引入了基于LLM的反馈机制以提升生成内容质量，同时提出一种基于LLM作为评判者的评估框架，用于评估新颖性、幻觉和可行性等关键指标。结果表明，在定性和定量评估中，结合GPT-4o mini与反馈机制的RAG方法表现最佳。此外，我们还进行了人工评估，验证了LLM在提取、生成及反馈环节的有效性。
原文摘要 · Abstract (English)
The Future Work section of a scientific article outlines potential research directions by identifying gaps and limitations of a current study. This section serves as a valuable resource for early-career researchers seeking unexplored areas and experienced researchers looking for new projects or collaborations. In this study, we generate future work suggestions from a scientific article. To enrich the generation process with broader insights and reduce the chance of missing important research directions, we use context from related papers using RAG. We experimented with various Large Language Models (LLMs) integrated into Retrieval-Augmented Generation (RAG). We incorporate an LLM feedback mechanism to enhance the quality of the generated content and introduce an LLM-as-a-judge framework for robust evaluation, assessing key aspects such as novelty, hallucination, and feasibility. Our results demonstrate that the RAG-based approach using GPT-4o mini, combined with an LLM feedback mechanism, outperforms other methods based on both qualitative and quantitative evaluations. Moreover, we conduct a human evaluation to assess the LLM as an extractor, generator, and feedback provider.
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