Large language models for medical consultation are often evaluated after a clinical problem has already been made clear, although real consultations may begin with a vague, minimized, or misframed concern. We evaluated three API models across four physician-authored, multi-turn vignettes under baseline and entry-to-car...
Yining Hua, C. Ayubcha, Hongbin Na et al.· 0 citations
CARE-Bench, a source-grounded benchmark that evaluates sequential patient-facing triage as a four-label per-turn current-action task, is introduced, which improves 10 of 11 models and supports explicit evaluation of action timing before deployment.
Intent-Aware Prompting (IAP) is proposed, a novel approach for detecting mental manipulations using large language models (LLMs), providing a deeper understanding of manipulative tactics by capturing the underlying intents of participants.
Jiayuan Ma, Hongbin Na, Zimu Wang et al.· International Conference on...· 24 citations· ⚡3
StagedWorkspace is proposed, a versioned workspace for knowledge-work agents that binds parsed records and review diffs to content hashes of the native files as they change, and motivates benchmarks that score evidence, staged edits, and submitted artifacts as explicit state transitions.
Yining Hua, Hongbin Na, Yifan Zhou et al.· 0 citations
This paper addresses SemEval-2026 Task 4 Track A: Narrative Story Similarity by reformulating it as an instruction-following generation problem, employing parameter-efficient fine-tuning via LoRA to adapt pretrained large language models for triplet-based narrative comparison and incorporating synthetic triplet samples...
Haotan Guo, Hongbin Na, Zimu Wang et al.· SemEval@ACL· 1 citation
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