Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.10862 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910209521221632 |
|---|---|
| author | Rorseth, Joel Godfrey, Parke Golab, Lukasz Srivastava, Divesh Szlichta, Jarek |
| author_facet | Rorseth, Joel Godfrey, Parke Golab, Lukasz Srivastava, Divesh Szlichta, Jarek |
| contents | This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models (LLMs) in data-driven applications. We leverage novel pruning strategies to efficiently identify a minimal set of rules that subsume all others. We further demonstrate novel applications of these rules for LLM safety, specifically to test the resiliency of safety training and effectiveness of adversarial prompt injections. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10862 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems Rorseth, Joel Godfrey, Parke Golab, Lukasz Srivastava, Divesh Szlichta, Jarek Computation and Language This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models (LLMs) in data-driven applications. We leverage novel pruning strategies to efficiently identify a minimal set of rules that subsume all others. We further demonstrate novel applications of these rules for LLM safety, specifically to test the resiliency of safety training and effectiveness of adversarial prompt injections. |
| title | RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.10862 |