MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models
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Computer Science > Computation and Language
arXiv:2609.38543 (cs)
[Submitted on 29 Sep 2026]
Title:MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models
Authors:Lukas Thede, Yash Kumar Atri, David Chen, Danielle Bitterman, Matthias Bethge, Tom Hartvigsen, Zeynep Akata
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Abstract:Constantly evolving real-world knowledge necessitates models to be updated continuously. Especially in medicine, as clinical evidence changes over time, outdated knowledge can pose safety risks. Existing evaluations of knowledge integration focus on factual recall, offering limited insight into whether newly integrated knowledge is actually usable. Our benchmark MedKIT (Medical Knowledge Integration and Transfer) provides a granular evaluation of how models integrate and apply knowledge under realistic sequences of clinical updates. Each instance corresponds to a factual update derived from clinical evidence, paired with targeted probes that assess transfer across lexical variation, relational transformations, compositional reasoning, and open-ended operationalization, as well as locality tests for knowledge preservation. Using MedKIT, we conduct a large-scale empirical study of 12 knowledge integration strategies across 5 diverse models, including both general-purpose and medical LLMs. Our results reveal a consistent gap between recall and usable knowledge: while most methods achieve strong gains on the original update task and under lexical variation, relational generalization is limited, and no method yields meaningful improvements on compositional or operational tasks. These findings highlight a fundamental challenge in knowledge integration and position MedKIT as a testbed for developing methods that make newly integrated knowledge more consistently usable across tasks and contexts.
| Comments: | Accepted at NeurIPS 2026 (Evaluations & Datasets Track) |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.38543 [cs.CL] |
| (or arXiv:2609.38543v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38543
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models, by Lukas Thede and 6 other authors
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