Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
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When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training.It is often conjectured that this can teach the model the behavior of hallucinating factually incorrect responses, as the model is trained to generate facts that are not grounded in its pre-existing knowledge.In this work, we study the impact of such exposure to new knowledge on the capability of the fine-tuned model to utilize its pre-existing knowledge.To this end, we design a controlled setup, focused on closedbook QA, where we vary the proportion of the fine-tuning examples that introduce new knowledge.We demonstrate that large language models struggle to acquire new factual knowledge through fine-tuning, as fine-tuning examples that introduce new knowledge are learned significantly slower than those consistent with the model's knowledge.However, we also find that as the examples with new knowledge are eventually learned, they linearly increase the model's tendency to hallucinate.Taken together, our results highlight the risk in introducing new factual knowledge through fine-tuning, and support the view that large language models mostly acquire factual knowledge through pre-training, whereas finetuning teaches them to use it more efficiently.
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Post-training should align the model to 'know what it knows' rather than add knowledge.
“We follow the principle that post-training should align the model to 'know what it knows' rather than add knowledge (Gekhman et al., 2024; Mielke et al., 2020).”
From The Llama 3 Herd of Models (2024) · cites this paper as Gekhman et al., 2024
Fine-tuning large language models on new information that was not acquired during pretraining can encourage the model to hallucinate.
“Finally, a recent work shows that fine-tuning LLMs on new information that was not acquired during pretraining can encourage the model to hallucinate [38].”
From Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey) (2024) · cites this paper as [38]
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- Authors
- Gekhman, Z., Yona, G., Aharoni, R., Eyal, M., Feder, A., Reichart, R., & Herzig, J.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Published
- 2024
- Pages
- 7765-7784
- OpenAlex
- W4404782801
- Cited by
- 86
- References
- 22
- Citations by year
- 2026: 31 · 2025: 38 · 2024: 17
- FWCI
- 24.83
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- hallucinations, large language models, supervised fine-tuning, closed-book question answering, factual grounding, knowledge distillation
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