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ALiiCE: Evaluating Positional Fine-grained Citation Generation

arXiv 2406.13375 ↗First posted 19 Jun 2024cs.CLcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 19 Jun 2024.

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28 of 41 references.

References · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  7. 2022A Survey on In-context Learningcited by 717
  8. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  9. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  10. 2019ELI5: Long Form Question Answeringcited by 567
  11. 2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontierscited by 466
  12. 2021Large Dual Encoders Are Generalizable Retrieverscited by 454
16 more
  1. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 385
  2. 2023Evaluating Verifiability in Generative Search Enginescited by 297
  3. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 231
  4. 2021Hurdles to Progress in Long-form Question Answeringcited by 166
  5. 2023Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Taskscited by 73
  6. 2023A Survey of Large Language Models Attributioncited by 71
  7. 2024Attribute First, then Generate: Locally-attributable Grounded Text Generationcited by 54
  8. 2023Understanding Retrieval Augmentation for Long-Form Question Answeringcited by 45
  9. 2023Effective Large Language Model Adaptation for Improved Grounding and Citation Generationcited by 44
  10. 2023"According to ...": Prompting Language Models Improves Quoting from Pre-Training Datacited by 42
  11. 2024Training Language Models to Generate Text with Citations via Fine-grained Rewardscited by 40
  12. 2022ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Datacited by 34
  13. 2023Retrieving Supporting Evidence for Generative Question Answeringcited by 31
  14. 2022CiteBench: A benchmark for Scientific Citation Text Generationcited by 22
  15. 2022CORWA: A Citation-Oriented Related Work Annotation Datasetcited by 17
  16. 2024Related Work and Citation Text Generation: A Surveycited by 17

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Citing papers per year: 2024: 2; 2025: 8; 2026: 5.

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  1. 2025Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Modelscited by 38
  2. 2024Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refusecited by 33
  3. 2024HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Routercited by 16
  4. 2025Training a Utility-based Retriever Through Shared Context Attribution for Retrieval-Augmented Language Modelscited by 13
  5. 2025Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Modelscited by 7
  6. 2026Gated Differentiable Working Memory for Long-Context Language Modelingcited by 6
  7. 2025Large Language Model Sourcing: A Surveycited by 5
  8. 2025Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citationcited by 4
  9. 2026Are Finer Citations Always Better? Rethinking Granularity for Attributed Generationcited by 2
  10. 2025MCiteBench: A Multimodal Benchmark for Generating Text with Citationscited by 1
  11. 2026Self-Manager: Parallel Agent Loop for Long-form Deep Researchcited by 1
  12. 2025FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answeringcited by 0
3 more
  1. 2025From Facts to Conclusions : Integrating Deductive Reasoning in Retrieval-Augmented LLMscited by 0
  2. 2026Explicit Evidence Grounding via Structured Inline Citation Generationcited by 0
  3. 2026ReCite: Agentic Reasoning for Faithful Citationcited by 0