Neural Retrievers are Biased Towards LLM-Generated Content
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Recently, the emergence of large language models (LLMs) has revolutionized the paradigm of information retrieval (IR) applications, especially in web search, by generating vast amounts of human-like texts on the Internet. As a result, IR systems in the LLM era are facing a new challenge: the indexed documents are now not only written by human beings but also automatically generated by the LLMs. How these LLM-generated documents influence the IR systems is a pressing and still unexplored question. In this work, we conduct a quantitative evaluation of IR models in scenarios where both human-written and LLM-generated texts are involved. Surprisingly, our findings indicate that neural retrieval models tend to rank LLM-generated documents higher. We refer to this category of biases in neural retrievers towards the LLM-generated content as the source bias. Moreover, we discover that this bias is not confined to the first-stage neural retrievers, but extends to the second-stage neural re-rankers. Then, in-depth analyses from the perspective of text compression indicate that LLM-generated texts exhibit more focused semantics with less noise, making it easier for neural retrieval models to semantic match. To mitigate the source bias, we also propose a plug-and-play debiased constraint for the optimization objective, and experimental results show its effectiveness. Finally, we discuss the potential severe concerns stemming from the observed source bias and hope our findings can serve as a critical wake-up call to the IR community and beyond. To facilitate future explorations of IR in the LLM era, the constructed two new benchmarks are available at https://github.com/KID-22/Source-Bias.
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Modern retrieval models tend to favor LLM-generated content over human-authored content.
“This integration is reshaping the dynamics of information retrieval, as evidenced by recent empirical studies [68, 339] suggesting that modern retrieval models tend to favor LLM-generated content over human-authored content.”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [68]
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Long, K., Li, S., Xu, C., Tang, J., & Wang, T. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730061
Impact of LLM-Modified Queries and Documents in Training Data on Neural Retrieval Models
Nakachi, Y., & Kato, M. P. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3767695.3769485
How Do LLM-Generated Texts Impact Term-Based Retrieval Models?
Huang, W., Bi, K., Cai, Y., Chen, W., Guo, J., & Cheng, X. (2026) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3773966.3777988
Training-Induced Bias Toward LLM-Generated Content in Dense Retrieval
Xion, W., & Nejdl, W. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21289-4_6
Turbocharging Brazilian Mergers and Acquisitions: Questions & Answers Evaluation
Rubin, F. S., Moura, P. N. D. S., & Alvim, A. C. F. (2026) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2026.5703
Ukaigwe, J. A., & Ezeanya, C. U. (2026) · Discover Education · doi:10.1007/s44217-026-01629-0
VulGen: Workshop on Vulnerabilities in Generative Systems for Information Retrieval
Sun, S., Lawati, S. F. D. A., Dietz, L., Khirbat, M., Mitra, B., de Rijke, M., & Spina, D. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808654
When LLM Judges Inflate Scores: Exploring Overrating in Relevance Assessment
Yu, C., Li, H., Zuccon, G., Mackenzie, J., & Leelanupab, T. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809905
How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews
Grossman, R., Liu, S., Chen, M. K., Smith, M., Borcea, C., & Chen, Y. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809667
Peng, Y., Liu, Y., Wang, Q., Zhang, L., Xie, C., Zhang, Y., & Mao, Z. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3837071
Self-citation
- Authors
- Dai, S., Zhou, Y., Pang, L., Liu, W., Hu, X., Liu, Y., Zhang, X., Wang, G., & Xu, J.
- Affiliations
- Renmin University of China (CN)
- Chinese Academy of Sciences (CN)
- Huawei Technologies (China) (CN)
- Venue
- Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2024-08-24
- Pages
- 526-537
- OpenAlex
- W4401863498
- Cited by
- 35
- References
- 26
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 5
- FWCI
- 13.19
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Youth Innovation Promotion Association CAS (2023111)
- Beijing Natural Science Foundation (4222029)
- National Natural Science Foundation of China (62276248, 62376275,62377044,62076234)
- National Key Research and Development Program of China (2023YFA1008704)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- source bias, LLM-generated content, neural retrieval, neural reranking, information retrieval, large language models, text compression, bias mitigation, benchmark design, semantic matching
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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