11 citations · 21 across the 11 of their papers we have counts for
9 papers · 1 filter
TextReasoningBench: Does Reasoning Really Improve Text Classification in Large Language Models?
Xinyu Guo, Yazhou Zhang, Jing Qin
Eliciting explicit, step-by-step reasoning traces from large language models (LLMs) has emerged as a dominant paradigm for enhancing model capabilities. Although such reasoning str…
NurValues: Real-World Nursing Values Evaluation for Large Language Models in Clinical Context
Ben Yao, Qiuchi Li, Yazhou Zhang +4
While LLMs have demonstrated medical knowledge and conversational ability, their deployment in clinical practice raises new risks: patients may place greater trust in LLM-generated…
Beyond Single-Sentence Prompts: Upgrading Value Alignment Benchmarks with Dialogues and Stories
Yazhou Zhang, Qimeng Liu, Qiuchi Li +2
Evaluating the value alignment of large language models (LLMs) has traditionally relied on single-sentence adversarial prompts, which directly probe models with ethically sensitive…
Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models
Yazhou Zhang, Chunwang Zou, Bo Wang +1
Sarcasm detection, as a crucial research direction in the field of Natural Language Processing (NLP), has attracted widespread attention. Traditional sarcasm detection tasks have t…
Edu-Values: Towards Evaluating the Chinese Education Values of Large Language Models
Peiyi Zhang, Yazhou Zhang, Bo Wang +3
In this paper, we present Edu-Values, the first Chinese education values evaluation benchmark that includes seven core values: professional philosophy, teachers' professional ethic…
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
Yazhou Zhang, Chunwang Zou, Zheng Lian +2
In the era of large language models (LLMs), the task of ``System I''~-~the fast, unconscious, and intuitive tasks, e.g., sentiment analysis, text classification, etc., have been ar…