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Background

  • Background This paper discusses the current issues of large language models (LLMs) directly generating literature summaries, such as missing key elements, a lack of comparative analysis, and a lack of organizational structure.

  • Existing Work The problems faced by existing work mainly arise from the window limitations of LLMs, which make generating a complete literature review challenging. This usually involves a two-step process of summarization and literature review generation that may lead to the loss of key information. Directly generated literature summaries by LLMs often lack comparative analysis and are discrete for each paper, without classification for similar works and an organized structure.

Core Contributions

  • Introduced an LLM agent named ChatCite

    • Challenge 1: Missing Key Elements The paper proposes a Key Element Extractor module to independently handle the proposed work description and reference paper set. By constructing seven simple guiding questions, the module helps the model to extract key elements.

    • Challenge 2: Lack of Comparative Analysis and Organizational Structure The paper introduces a Reflective Incremental Generator module to overcome the challenges. It uses a Comparative Summarizer and a Reflective Evaluator to generate literature summaries for each reference in the set iteratively, selecting the best summary through a voting mechanism.

Implementation and Deployment

The paper designs a system named ChatCite, consisting of two modules to address the challenges. The Key Element Extractor module extracts key content from the literature using guiding questions, and the Reflective Incremental Generator module combines the previous summary generated by the LLM agent with the key elements of the proposed work and reference papers. It employs a Reflective Evaluator to vote for the best candidate result in each round. The process iterates repeatedly until all papers in the reference set have been processed. The final output is selected based on the highest voting score of the related work summaries.

Summary

The ChatCite system is designed to overcome the challenges faced by LLMs in generating literature reviews. It enables an LLM agent to more effectively understand, summarize, and compare different research works, thus producing organized and comparative literature reviews.