@inproceedings{soni-etal-2025-evaluation,
    title = "Evaluation of {LLM}s-based Hidden States as Author Representations for Psychological Human-Centered {NLP} Tasks",
    author = "Soni, Nikita  and
      Chitale, Pranav  and
      Singh, Khushboo  and
      Balasubramanian, Niranjan  and
      Schwartz, H.",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.426/",
    pages = "7658--7667",
    ISBN = "979-8-89176-195-7",
    abstract = "Like most of NLP, models for human-centered NLP tasks{---}tasks attempting to assess author-level information{---}predominantly use rep-resentations derived from hidden states of Transformer-based LLMs. However, what component of the LM is used for the representation varies widely. Moreover, there is a need for Human Language Models (HuLMs) that implicitly model the author and provide a user-level hidden state. Here, we systematically evaluate different ways of representing documents and users using different LM and HuLM architectures to predict task outcomes as both dynamically changing states and averaged trait-like user-level attributes of valence, arousal, empathy, and distress. We find that representing documents as an average of the token hidden states performs the best generally. Further, while a user-level hidden state itself is rarely the best representation, we find its inclusion in the model strengthens token or document embeddings used to derive document- and user-level representations resulting in best performances."
}