KARLA: Knowledge-base Augmented Retrieval for Language Models
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Computer Science > Artificial Intelligence
Title:KARLA: Knowledge-base Augmented Retrieval for Language Models
Abstract:We propose a new method that allows an LLM to automatically pull in factual knowledge from a knowledge base during token generation. This means that (1)~factual knowledge in the LLM output can be updated without retraining the LLM, (2)~facts in the LLM output can be traced to the knowledge base for transparency and explainability, and (3)~smaller models can achieve the same factual accuracy as larger models. Our core idea is to train the model to produce special tokens that trigger a query to the knowledge base. Our experiments show that our method improves factual grounding in both short and long-form generation, and allows factual revisions to take effect through KB edits rather than parameter updates.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.26807 [cs.AI] |
| (or arXiv:2606.26807v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26807
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Francois Crespin [view email] [via CCSD proxy][v1] Thu, 25 Jun 2026 09:44:40 UTC (2,572 KB)
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