The DOI matches its Crossref record; 1 of the 8 references it cites were found in a registry or an index, and 7 are not checked yet.
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
Open accessAbstract
Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors.One avenue in reaching this goal is basing the answers on reliable sources.However, this Evidence-Based QA has proven to work insufficiently with LLMs in terms of citing the correct sources (source quality) and truthfully representing the information within sources (answer attributability).In this work, we systematically investigate how to robustly fine-tune LLMs for better source quality and answer attributability.Specifically, we introduce a data generation pipeline with automated data quality filters, which can synthesize diversified high-quality training and testing data at scale.We further introduce four test sets to benchmark the robustness of fine-tuned specialist models.Extensive evaluation shows that fine-tuning on synthetic data improves performance on both in-and out-of-distribution.Furthermore, we show that data quality, which can be drastically improved by proposed quality filters, matters more than quantity in improving Evidence-Based QA.
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“Recent NLP studies have emphasized measuring how well model outputs stay supported by provided data (i.e., faithfulness) [2], [5], but this has not been systematically applied to intrusion detection scenarios.”
From Can Large Language Models Be Trusted for AI-Based Intrusion Detection in Cybersecurity? An Evidence-Grounded Log Analysis (2026) · cites this paper as [5]
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“However, existing studies largely evaluate LLMs based on detection performance or qualitative usefulness, without rigorously assessing whether their explanations are grounded in actual log evidence.”
From Can Large Language Models Be Trusted for AI-Based Intrusion Detection in Cybersecurity? An Evidence-Grounded Log Analysis (2026) · cites this paper as [5]
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Research works printed but not linked: 6
- Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo, Lijie Wen, Philip S. Yu, and Zhijiang Guo. 2023. Do large language models know about facts? ArXiv, abs/2310.05177. · arXiv
- Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. ArXiv:2004.04906 [cs. · arXiv
- Dongfang Li, Zetian Sun, Xinshuo Hu, Zhenyu Liu, Ziyang Chen, Baotian Hu, Aiguo Wu, and Min Zhang. 2023. A Survey of Large Language Mod- els Attribution. ArXiv:2311.03731 [cs]. · arXiv
- Nelson F. Liu, Tianyi Zhang, and Percy Liang. 2023a. Evaluating Verifiability in Generative Search Engines. ArXiv:2304.09848 [cs]. · arXiv
- Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instruc- tions with human feedback. Advances in Neural Information Processing Systems, 35:27730-27744.
- Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar San- seviero, Alexander M. Rush, and Thomas Wolf. 2023. Zephyr: Direct Distillation of LM Alignment. ArXiv:2310.16944 [cs. · arXiv
Cited by
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Senni, C. C., Schimanski, T., Bingler, J., Ni, J., & Leippold, M. (2024) · Environmental Research Communications · doi:10.1088/2515-7620/ad9e88 · Cited by 19
Self-citationJiang, H., Shi, S., Yao, Y., Jiang, C., & Li, Q. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3633887 · Cited by 13
Gehricke, S. A., Leippold, M., & Schimanski, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5246456 · Cited by 6
Self-citationGuo, S., Liu, H., Chen, X., Xie, Y., Zhang, L., Han, T., Chen, H., Chang, Y., & Wang, Y.-X. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737254 · Cited by 3
Balancing 3D-model fidelity for training a vehicle detector on simulated data
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Metadata
- Authors
- Schimanski, T., Ni, J., Kraus, M., Ash, E., & Leippold, M.
- Affiliations
- University of Zurich (CH)
- University of Regensburg (DE)
- ETH Zurich (CH)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 1913-1931
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402671969
- Cited by
- 15
- References
- 0
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 2
- FWCI
- 4.43
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (207800)
- Hasler Stiftung
- 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, LLM fine-tuning, source quality, synthetic training data, data quality filtering, robustness, out-of-distribution generalization, benchmark dataset
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