KoRe: Compact Knowledge Representations for Large Language Models
Modern Large Language Models (LLMs) have shown impressive performances in user-facing tasks such as question answering, as well as consistent improvements in reasoning capabilities. Still, the way these models encode knowledge seems inherently flawed: by design, LLMs encode world-knowledge within their parameters. This way of representing knowledge is inherently opaque, difficult to debug and update, and prone to hallucinations. On the other hand, Knowledge Graphs can provide human-readable and easily editable world knowledge representations, and their application in knowledge-intensive tasks has consistently proven beneficial to downstream performance. Nonetheless, current integration te...
| Authors | Davide Cavicchini, Fausto Giunchiglia, Jacopo Staiano |
|---|---|
| Primary category | cs.CL |
| Published | 19 May 2026 |
| Last updated at source | 22 July 2026 |
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