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Tutoring Large Language Models to be Domain-adaptive, Precise and Safe — arXiv:2609.23071

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23071
License (as archived): CC0-1.0
Archived: 2026-10-09T06:50:32.345Z
SHA-256 of the archived text: 14b92b43825f625b29776aa0ea5c5f0442a3adcdaf077b254b4e3e3f6b576d03
Stable URL of this page: https://metraipolis.com/documento/dc_3a59cd2bab39

How to cite

Tutoring Large Language Models to be Domain-adaptive, Precise and Safe — arXiv:2609.23071 — arXiv (en), https://arxiv.org/abs/2609.23071 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3a59cd2bab39 (Metraipolis archive dc_3a59cd2bab39, sha256 14b92b43825f625b29776aa0ea5c5f0442a3adcdaf077b254b4e3e3f6b576d03)

BibTeX

@misc{dc3a59cd2bab39,
  title = {Tutoring Large Language Models to be Domain-adaptive, Precise and Safe — arXiv:2609.23071},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23071},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3a59cd2bab39; sha256 14b92b43825f625b29776aa0ea5c5f0442a3adcdaf077b254b4e3e3f6b576d03},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23071. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This thesis proposes a framework for "responsible intelligence" to address AI's critical challenges in safety, ethics, and cultural sensitivity. It advances three core areas: First, it improves domain adaptation in specialized fields using active learning and graph-based knowledge to reduce hallucinations. Second, it enhances ethical rigor via a novel decoding-time alignment mechanism that proactively blocks harmful text generation in real-time. Finally, it ensures cultural and multilingual safety through language-specific steering that respects diverse linguistic and social norms. Ultimately, this work provides a blueprint for building next-generation AI that is contextually knowledgeable, ethically sound, and culturally adaptable.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.CL).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.23071 — 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 2026-10-09T06:48:45.115Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).