[2024 Best AI Paper] LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA

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This video was created using https://paperspeech.com. If you’d like to create explainer videos for your own papers, please visit the website! Title: LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA Authors: Jiajie Zhang, Yushi Bai, Xin Lv, Wanjun Gu, Danqing Liu, Minhao Zou, Shulin Cao, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li Abstract: Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to their potential hallucinations. In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations, improving their faithfulness and verifiability. We first introduce LongBench-Cite, an automated benchmark for assessing current LLMs' performance in Long-Context Question Answering with Citations (LQ