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Are LLM Assistants Regulatory Ready? Answers from regenold's 2026 EU AI Act Q&A Challenge — arXiv:2611.04546

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04546
License (as archived): CC0-1.0
Archived: 2026-11-04T11:50:16.505Z
SHA-256 of the archived text: 007cae2a363896d692837c89f1c1c4eb9dad62a4278c8b39a56d4816dfecaf68
Stable URL of this page: https://metraipolis.com/documento/dc_cee3cedfb756

How to cite

Are LLM Assistants Regulatory Ready? Answers from regenold's 2026 EU AI Act Q&A Challenge — arXiv:2611.04546 — arXiv (en), https://arxiv.org/abs/2611.04546 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cee3cedfb756 (Metraipolis archive dc_cee3cedfb756, sha256 007cae2a363896d692837c89f1c1c4eb9dad62a4278c8b39a56d4816dfecaf68)

BibTeX

@misc{dccee3cedfb756,
  title = {Are LLM Assistants Regulatory Ready? Answers from regenold's 2026 EU AI Act Q&A Challenge — arXiv:2611.04546},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04546},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cee3cedfb756; sha256 007cae2a363896d692837c89f1c1c4eb9dad62a4278c8b39a56d4816dfecaf68},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04546. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language model (LLM) assistants are increasingly proposed for regulatory affairs, yet their reliability in regulatory tasks remains unclear. We introduce the EU AI Act Q&A Benchmark, an expert-curated benchmark of 110 open-ended questions about the EU AI Act (Regulation (EU) 2024/1689). Each question comes with an exemplary answer, question-specific correctness criteria, and expected references. Evaluation measures answer correctness, reference quality, conciseness, tone, and latency, with a multi-turn ``hard mode'' testing responses to simulated user pushback. We also present the results of the 2026 EU AI Act Q&A Challenge, based on the benchmark, where three off-the-shelf baselines and five purpose-built assistants (from four commercial developers and one academic entrant) were tested. Only two of the five submitted assistants consistently outperformed the baselines; one achieved high scores across all evaluated metrics. These results show that specialist development alone does not guarantee better performance and highlight the critical importance of LLM assistant evaluation in regulatory affairs. The benchmark code is openly available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2611.04546 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T11:50:11.438Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).