Summary of Citekit: a Modular Toolkit For Large Language Model Citation Generation, by Jiajun Shen et al.
Citekit: A Modular Toolkit for Large Language Model Citation Generation
by Jiajun Shen, Tong Zhou, Yubo Chen, Kang Liu
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a unified framework for standardizing and comparing different citation generation methods in Large Language Models (LLMs) used in Question-Answering (QA) tasks. The proposed toolkit, Citekit, is an open-source and modular platform designed to facilitate the implementation and evaluation of existing methods while fostering the development of new approaches to improve citation quality. The tool consists of four main modules and 14 components that can be combined to construct a pipeline for evaluating an existing method or innovative designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, the paper helps create a standardized way for large language models to generate citations in question-answering tasks, making it easier to compare and improve different methods. The Citekit toolkit is designed to make this process more efficient and effective by providing a modular platform that can be customized to suit different needs. |
Keywords
» Artificial intelligence » Question answering
