Skip to content
DraconiticResearch
Search Sources API
197,785 studies31 sourcesUpdated 54 min ago
PreprintAll rights reservedbioRxiv

Large Language Models Predict Human Social Behavior via Interpretable Mechanisms

  1. 1State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences

Related

  1. Discovering compositional, interpretable structure in social behavior
    Y. Wan, S. Koukuntla, A. El Hady +2
    bioRxivPreprintCC BY-NC-NDFull textZoology & animal behaviourbioRxiv
  2. Movement mechanisms explain current animal density patterns and predict their reorganization under global change
    R. Bauknecht, W. Rogers, A. T. Pearse +1
    bioRxivPreprintAll rights reservedFull textEcologybioRxiv
  3. Individual-level social associations and social information use predict foraging success in winter mixed-species flocks
    F. Hafeez, L. N. Vander Meiden, A. E. Hand +4
    bioRxivPreprintCC BY-NC-NDFull textZoology & animal behaviourbioRxiv
  4. Dynamics and geometry of emotion and cognition: an interpretable model of individual human behavior
    J. Hong, E. M. Nester, L. Varisa +9
    bioRxivPreprintCC BY-NC-NDFull textNeurosciencebioRxiv
  5. Interpretable Symptom Vectors for Depression in a Large Language Model
    Fangyi Zhu, Ajay Subramanian, Allison Constant +3
    arXivPreprintarXiv licenceFull textNeurosciencearXiv
BibTeX
@misc{ye2026large,
  title = {{Large Language Models Predict Human Social Behavior via Interpretable Mechanisms}},
  author = {Y. Ye and Y. Fang and X. Gao and V. Kurtz-David and A. Hula and C. Fan and T. Long and B. Shen},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.06.749667},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.06.749667v1},
}
RIS
TY  - UNPB
TI  - Large Language Models Predict Human Social Behavior via Interpretable Mechanisms
AU  - Y. Ye
AU  - Y. Fang
AU  - X. Gao
AU  - V. Kurtz-David
AU  - A. Hula
AU  - C. Fan
AU  - T. Long
AU  - B. Shen
PY  - 2026
DA  - 2026/09/06
PB  - bioRxiv
DO  - 10.64898/2026.09.06.749667
UR  - https://www.biorxiv.org/content/10.64898/2026.09.06.749667v1
ER  -