Evaluation of LLMs-based Hidden States as Author Representations for Psychological Human-Centered NLP Tasks
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912490142564352 |
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| author | Soni, Nikita Chitale, Pranav Singh, Khushboo Balasubramanian, Niranjan Schwartz, H. Andrew |
| author_facet | Soni, Nikita Chitale, Pranav Singh, Khushboo Balasubramanian, Niranjan Schwartz, H. Andrew |
| contents | Like most of NLP, models for human-centered NLP tasks -- tasks attempting to assess author-level information -- predominantly use representations 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00124 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluation of LLMs-based Hidden States as Author Representations for Psychological Human-Centered NLP Tasks Soni, Nikita Chitale, Pranav Singh, Khushboo Balasubramanian, Niranjan Schwartz, H. Andrew Computation and Language Artificial Intelligence Human-Computer Interaction Machine Learning Like most of NLP, models for human-centered NLP tasks -- tasks attempting to assess author-level information -- predominantly use representations 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. |
| title | Evaluation of LLMs-based Hidden States as Author Representations for Psychological Human-Centered NLP Tasks |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2503.00124 |