activity
20232026
most citedPushing The Limit of LLM Capacity for Text Classification

11 citations · 34 across the 8 of their papers we have counts for

collaborators
Showing cs.CLShow all

6 papers · 1 filter

cs.CL2025

Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks

Wenya Xie, Qingying Xiao, Yu Zheng +8

The rise of large language models (LLMs) has transformed healthcare by offering clinical guidance, yet their direct deployment to patients poses safety risks due to limited domain…

cs.CL2025★ 1 cited

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…

cs.CL2024★ 4 cited

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…

cs.CL2024★ 7 cited

Is Your LLM Outdated? A Deep Look at Temporal Generalization

Chenghao Zhu, Nuo Chen, Yufei Gao +3

The rapid advancement of Large Language Models (LLMs) has led to the development of benchmarks that consider temporal dynamics, however, there remains a gap in understanding how we…

cs.CL2024★ 11 cited

Pushing The Limit of LLM Capacity for Text Classification

Yazhou Zhang, Mengyao Wang, Chenyu Ren +4

The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) acro…

cs.CL2023★ 10 cited

DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations

Yazhou Zhang, Mengyao Wang, Youxi Wu +4

Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new visi…