@inproceedings{nam-etal-2026-idealign,
    title = "{IDEA}lign: Comparing Ideas of Large Language Models to Domain Experts",
    author = "Nam, HyunJi  and
      Langlois, Luc{\'i}a  and
      Malamut, Jim  and
      Tan, Mei  and
      Demszky, Dorottya",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.182/",
    pages = "3908--3925",
    ISBN = "979-8-89176-380-7",
    abstract = "Large language models (LLMs) are increasingly used to produce open-ended, interpretive annotations, yet there is no validated, scalable measure of ***idea-level similarity*** to expert annotations. We (i) introduce the content evaluation of LLM annotations as a core, understudied task, (ii) propose IDEAlign for capturing expert similarity judgments via the odd-one-out tasks, and (iii) benchmark various similarity methods, such as text embeddings, topic models, and LLM-as-a-judge, against these human ratings. Applying this approach to two real-world educational datasets (interpreting math reasoning and feedback generation), we find that most metrics fail to capture the nuanced dimensions of similarity meaningful to experts. LLM-as-a-judge performs best (11{--}18{\%} improvement over other methods) but still falls short of expert alignment, making it useful as a triage filter rather than a substitute for human review. Our work demonstrates the difficulty of evaluating open-ended LLM annotations at scale, and positions IDEAlign as a reusable protocol for benchmarking on this task, thereby informing responsible deployment of LLMs."
}