Summary of Kg-ctg: Citation Generation Through Knowledge Graph-guided Large Language Models, by Avinash Anand et al.
KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models
by Avinash Anand, Mohit Gupta, Kritarth Prasad, Ujjwal Goel, Naman Lal, Astha Verma, Rajiv Ratn Shah
First submitted to arxiv on: 15 Apr 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 This paper presents a framework for Citation Text Generation (CTG), which aims to produce accurate citations of referenced documents within a source document. Building upon previous work in text summarization, the authors demonstrate the use of Large Language Models (LLMs) for CTG and show improvements by incorporating knowledge graph relations into the LLM prompts. The study uses a subset of the S2ORC dataset, consisting of computer science research papers in English, to evaluate model performance. Results indicate that Vicuna performs best with 14.15 Meteor, 12.88 Rouge-1, 1.52 Rouge-2, and 10.94 Rouge-L, while Alpaca improves performance by 36.98% in Rouge-1 and 33.14% in Meteor when incorporating knowledge graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us generate accurate citations for research papers. It uses special language models to write citations that are relevant and correct. The authors test their model on a set of computer science papers and find that it works well, with Vicuna being the best performer. They also show that adding extra information from knowledge graphs can improve the results even more. |
Keywords
» Artificial intelligence » Knowledge graph » Rouge » Summarization » Text generation
