This paper explores the task of generating citation texts in research papers. To accurately understand and capture relevant features from scientific papers, the authors leverage the synthesis of knowledge graphs. They present a compelling use case for employing Large Language Models (LLMs) in the domain of citation text generation, demonstrating their impressive performance when given the source abstract and target abstract, introduction, and conclusion. The efficiency of LLMs is substantiated through automatic evaluations employing various metrics. The experiments also emphasize the significance of utilizing knowledge graphs as prompts to guide the model's generation process. The authors fine-tuned three LLMs - LLaMA, Alpaca, and Vicuna - for the task of citation text generation, and found that Vicuna performs the best without knowledge graphs, while Alpaca exhibits superior performance when knowledge graphs are incorporated, with a 33.14% increase in METEOR and 36.98% increase in Rouge-1 score. The paper highlights the value of leveraging LLMs and incorporating knowledge graphs to enhance the generation of accurate and contextually appropriate citation text for scientific papers.
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