Interpreting the causes of hallucinations in large language models

Develop interpretability methods and causal diagnostics that explain why large language models hallucinate, including identifying query- and model-specific mechanisms that lead to hallucinations in retrieval-augmented generation systems used for legal research.

Background

Despite improvements from retrieval-augmented generation, the study documents persistent hallucinations, including misinterpretation of case holdings, misuse of authority, and fabrication. These varied failure modes suggest deeper explanatory gaps about when and why models produce incorrect or misleading outputs.

Because proprietary systems reveal limited technical details and mix multiple components (retrieval, filtering, generation), pinpointing the causal mechanisms behind hallucinations remains difficult. The authors introduce a typology of RAG-related errors to aid analysis, but explicitly note that interpreting why models hallucinate is still an open problem.

References

Interpreting why an LLM hallucinates is an open problem.

— Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools  (2405.20362 - Magesh et al., 2024) in Section 6.4 (A Typology of Legal RAG Errors)

Although xAI has made progress in revealing how LLMs produce outputs, we still lack clear insight into why specific hallucinations emerge.

— Do Large Language Models Hallucinate Electric Fata Morganas?  (2608.18816 - Šekrst, 19 Aug 2026) in Section 3, “Causes of Hallucinations”

For instance, although the Phi-4 model, which has approximately twice as many parameters as Llama 3.1 8B Instruct and Mistral 7B Instruct, exhibits notably different behavior, it remains unclear whether these differences are attributable to model size or other architectural factors.

— Detecting Hallucination in LLMs: Tracing the Topological Signatures of Impaired Context Sharing  (2609.21096 - Jalilifard et al., 17 Sep 2026) in Section 6, Conclusion

Still, it remains an open question how internal model dynamics differ when hallucination is caused by genuine knowledge gaps rather than misaligned context sharing.

— Detecting Hallucination in LLMs: Tracing the Topological Signatures of Impaired Context Sharing  (2609.21096 - Jalilifard et al., 17 Sep 2026) in Section 6, Conclusion

We hypothesize that confident hallucination may generalize beyond medicine; any domain where models have strong parametric priors and receive outcome-only rewards could exhibit this pattern.

— MedAgent-R1: Faithfulness-Aware Reinforcement Learning for Evidence-Grounded Medical Reasoning  (2608.30676 - Chen et al., 31 Aug 2026) in Appendix, Section ‘Extended Discussion’, paragraph ‘Generalizability beyond medicine’