Alignment of Large Language Models with Human Preferences and Values

Usman Naseem, Gautam Siddharth Kashyap, Kaixuan Ren, Yiran Zhang, Utsav Maskey, Juan Ren, Afrozah Nadeem


Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their reliability and alignment with human expectations remain unresolved challenges. This tutorial introduces the foundations of alignment and provides participants with a conceptual and practical understanding of the field. Core principles such as values, safety, reasoning, and pluralism will be presented through intuitive explanations, worked examples, and case studies. The aim is to equip attendees with the ability to reason about alignment goals, understand how existing methods operate in practice, and critically evaluate their strengths and limitations.
Anthology ID:
2025.alta-main.20
Volume:
Proceedings of The 23rd Annual Workshop of the Australasian Language Technology Association
Month:
November
Year:
2025
Address:
Sydney, Australia
Editors:
Jonathan K. Kummerfeld, Aditya Joshi, Mark Dras
Venue:
ALTA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
245
Language:
URL:
https://preview.aclanthology.org/ingest-alta/2025.alta-main.20/
DOI:
Bibkey:
Cite (ACL):
Usman Naseem, Gautam Siddharth Kashyap, Kaixuan Ren, Yiran Zhang, Utsav Maskey, Juan Ren, and Afrozah Nadeem. 2025. Alignment of Large Language Models with Human Preferences and Values. In Proceedings of The 23rd Annual Workshop of the Australasian Language Technology Association, pages 245–245, Sydney, Australia. Association for Computational Linguistics.
Cite (Informal):
Alignment of Large Language Models with Human Preferences and Values (Naseem et al., ALTA 2025)
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PDF:
https://preview.aclanthology.org/ingest-alta/2025.alta-main.20.pdf