KARLA: Knowledge-base Augmented Retrieval for Language Models
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 pa...
| Authors | Francois Crespin, Fabian M. Suchanek, Nils Holzenberger |
|---|---|
| Primary category | cs.AI |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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