@inproceedings{wu-etal-2026-thinking,
    title = "Thinking Alignment of Scenario-Oriented User Simulation",
    author = "Wu, Xiaoting  and
      Huang, Yi  and
      Gao, Chunyang  and
      Guo, Mengfei  and
      Yao, Jingyu  and
      Feng, Junlan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1989/",
    pages = "42932--42945",
    ISBN = "979-8-89176-390-6",
    abstract = "Existing user simulators based on prompting to role-play or SFT are generally confined to imitating users' textual utterances, without adequately considering the multi-faceted cognitive processes that underlie human decision-making during interactions. To facilitate better alignment with real human thinking patterns, we construct the LMSYS-UserThinking dataset, in which we augment 51k human{--}LLM conversations by reconstructing the user{'}s inner reasoning both during and at the end of each dialogue. Furthermore, to enhance controllability and situational coherence, we introduce scenario settings that describe the global context and user goals throughout multi-turn conversations. Using this dataset, we train user simulators called ThinkingUS on different base models. We evaluate our approach from both offline and online user simulation perspectives, ultimately demonstrating its effectiveness."
}