@inproceedings{janiak-etal-2026-rethinking,
    title = "Rethinking the Evaluation of Alignment Methods: Insights into Diversity, Generalisation, and Safety",
    author = "Janiak, Denis  and
      Moska, Julia  and
      Motyka, Dawid  and
      Seweryn, Karolina  and
      Walkowiak, Pawe{\l}  and
      {\.Z}uk, Bartosz  and
      Janz, Arkadiusz",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.7/",
    pages = "92--109",
    ISBN = "979-8-89176-383-8",
    abstract = "Large language models (LLMs) require careful alignment to balance competing objectives: factuality, safety, conciseness, proactivity, and diversity. Existing studies focus on individual techniques or specific dimensions, lacking a holistic assessment of the inherent trade-offs. We propose a unified evaluation framework that compares LLM alignment methods (PPO, DPO, ORPO, KTO) across these five axes, using both in-distribution and out-of-distribution datasets. Leveraging a specialized LLM-as-Judge prompt, validated through human studies, we reveal that DPO and KTO excel in factual accuracy, PPO and DPO lead in safety, and PPO best balances conciseness with proactivity. Our findings provide insights into trade-offs of common alignment methods, guiding the development of more balanced and reliable LLMs."
}