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Attribute or Abstain: Large Language Models as Long Document Assistants
Open accessAbstract
LLMs can help humans working with long documents, but are known to hallucinate.Attribution can increase trust in LLM responses: The LLM provides evidence that supports its response, which enhances verifiability.Existing approaches to attribution have only been evaluated in RAG settings, where the initial retrieval confounds LLM performance.This is crucially different from the long document setting, where retrieval is not needed, but could help.Thus, a long document specific evaluation of attribution is missing.To fill this gap, we present LAB, a benchmark of 6 diverse long document tasks with attribution, and experiments with different approaches to attribution on 5 LLMs of different sizes.We find that citation, i.e. response generation and evidence extraction in one step, performs best for large and fine-tuned models, while additional retrieval can help for small, prompted models.We investigate whether the "Lost in the Middle" phenomenon exists for attribution, but do not find this.We also find that evidence quality can predict response quality on datasets with simple responses, but not so for complex responses, as models struggle with providing evidence for complex claims.We release code and data for further investigation 1 .
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“Moreover, the citations are generated conditioned on the response, which has been shown to be more effective compared to prior or post-hoc citations (Buchmann et al., 2024).”
From In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (2025) · cites this paper as Buchmann et al., 2024
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“This finding is also consistent with related work such as Buchmann et al. (2024).”
From PeerQA: A Scientific Question Answering Dataset from Peer Reviews (2025) · cites this paper as Buchmann et al. (2024)
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Cited by
Sorted by citations
Song, M., Sim, S. H., Bhardwaj, R., Chieu, H. L., Majumder, N., & Poria, S. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2409.11242 · Cited by 22
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
Tan, Z., Yan, J., Hsu, I.-H., Han, R., Wang, Z., Le, L., Song, Y., Chen, Y., Palangi, H., Lee, G., et al. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.413 · Cited by 16
On Mechanistic Circuits for Extractive Question-AnsweringPreprint
Basu, S., Morariu, V. I., Wang, Z., Rossi, R. A., Zhao, C., Feizi, S., & Manjunatha, V. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2502.08059 · Cited by 3
Ojha, A. (2026) · American Journal of Financial Technology and Innovation · doi:10.54536/ajfti.v4i1.8551 · Cited by 2
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
Tan, Z., Yan, J., Hsu, I.-H., Han, R., Wang, Z., Le, L. T., Song, Y., Chen, Y., Palangi, H., Lee, G., et al. (2025) · arXiv · Cited by 54
Citation Failure: Definition, Analysis and Efficient Mitigation
Buchmann, J., & Gurevych, I. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.736
Self-citationOh, D., Ahn, S., & Lee, Y. (2026) · Journal of The Korean Institute of Defense Technology · doi:10.52682/jkidt.2026.8.1.012
Zhao, Z., & Wei, G. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.04286
Chen, H., & Li, Y. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.27064
From capability to assertability: epistemic regulation of LLM-mediated claim presentation
Wu, Q. (2026) · Ethics and Information Technology · doi:10.1007/s10676-026-09922-0
Calibrated Evidence Inference With a Small, Permissively Licensed Open ModelPreprint
Li, J. E. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2610.07019
Temporal Evidence Allocation For Longitudinal Clinical Retrieval: A Position And Technical ReportPreprint
Sahoo, S. R., Bharti, D., & Sasidharan, S. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2610.27682
Quezada, K. M. R., Cabada, R. R. A., & Salcedo, J. R. P. (2026) · Journal of Multidisciplinary Novel Journeys & Explorations · doi:10.63688/xbkk5h13
Metadata
- Authors
- Buchmann, J., Liu, X., & Gurevych, I.
- Affiliations
- Technische Universität Darmstadt (DE)
- Venue
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Published
- 2024
- Pages
- 8113-8140
- OpenAlex
- W4404782773
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 3
- FWCI
- 0.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- dx.doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Technischen Universität Darmstadt (submittedVersion, cc-by)
- Technischen Universität Darmstadt (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Technischen Universität Darmstadt (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, attribution, long documents, hallucinations, evidence extraction, citation, retrieval-augmented generation, quality of evidence, response quality, LLM benchmark
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