@inproceedings{xu-etal-2025-aliice,
title = "{AL}ii{CE}: Evaluating Positional Fine-grained Citation Generation",
author = "Xu, Yilong and
Gao, Jinhua and
Yu, Xiaoming and
Bi, Baolong and
Shen, Huawei and
Cheng, Xueqi",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.23/",
doi = "10.18653/v1/2025.naacl-long.23",
pages = "545--561",
ISBN = "979-8-89176-189-6",
abstract = "Large Language Model (LLM) can enhance its credibility and verifiability by generating text with citations. However, existing research on citation generation is predominantly limited to sentence-level statements, neglecting the significance of positional fine-grained citations that can appear anywhere within sentences. To facilitate further exploration of the positional fine-grained citation generation, we propose ALiiCE, the first automatic evaluation framework for this task. Our method employs a dependency tree based approach to parse the sentence-level claim into atomic claims. Then ALiiCE evaluates citation quality using three metrics, including positional fine-grained citation recall, precision, and coefficient of variation of citation positions. We evaluate the positional fine-grained citation generation performance of several LLMs on long-form QA datasets. Our experiments and analyses demonstrate the effectiveness and reasonableness of ALiiCE. We offer our insights into the current advancements and future directions for the positional fine-grained citation generation task."
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%0 Conference Proceedings
%T ALiiCE: Evaluating Positional Fine-grained Citation Generation
%A Xu, Yilong
%A Gao, Jinhua
%A Yu, Xiaoming
%A Bi, Baolong
%A Shen, Huawei
%A Cheng, Xueqi
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F xu-etal-2025-aliice
%X Large Language Model (LLM) can enhance its credibility and verifiability by generating text with citations. However, existing research on citation generation is predominantly limited to sentence-level statements, neglecting the significance of positional fine-grained citations that can appear anywhere within sentences. To facilitate further exploration of the positional fine-grained citation generation, we propose ALiiCE, the first automatic evaluation framework for this task. Our method employs a dependency tree based approach to parse the sentence-level claim into atomic claims. Then ALiiCE evaluates citation quality using three metrics, including positional fine-grained citation recall, precision, and coefficient of variation of citation positions. We evaluate the positional fine-grained citation generation performance of several LLMs on long-form QA datasets. Our experiments and analyses demonstrate the effectiveness and reasonableness of ALiiCE. We offer our insights into the current advancements and future directions for the positional fine-grained citation generation task.
%R 10.18653/v1/2025.naacl-long.23
%U https://aclanthology.org/2025.naacl-long.23/
%U https://doi.org/10.18653/v1/2025.naacl-long.23
%P 545-561
Markdown (Informal)
[ALiiCE: Evaluating Positional Fine-grained Citation Generation](https://aclanthology.org/2025.naacl-long.23/) (Xu et al., NAACL 2025)
ACL
- Yilong Xu, Jinhua Gao, Xiaoming Yu, Baolong Bi, Huawei Shen, and Xueqi Cheng. 2025. ALiiCE: Evaluating Positional Fine-grained Citation Generation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 545–561, Albuquerque, New Mexico. Association for Computational Linguistics.