11 citations · 34 across the 8 of their papers we have counts for
8 papers
GeoSym127K: Scalable Symbolically-verifiable Synthesis for Multimodal Geometric Reasoning
Jinhao Jing, Zheng Ma, Jinwei Liang +9
Large Multimodal Models (LMMs) often struggle with geometric reasoning due to visual hallucinations and a lack of mathematically precise Chain-of-Thought (CoT) data. To address thi…
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…
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…
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…
Mixture of Latent Experts Using Tensor Products
Zhan Su, Fengran Mo, Prayag Tiwari +3
In multi-task learning, the conventional approach involves training a model on multiple tasks simultaneously. However, the training signals from different tasks can interfere with…
Is Your LLM Outdated?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…
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…
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…