484 citations · 515 across the 26 of their papers we have counts for
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cs.AI2025
MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination
Ao Jia, Haiming Wu, Guohui Yao +3
Large language models (LLMs) are prone to three types of hallucination: Input-Conflicting, Context-Conflicting and Fact-Conflicting hallucinations. The purpose of this study is to…
cs.AI2025
Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection
Yazhou Zhang, Chunwang Zou, Bo Wang +2
Multimodal sarcasm understanding is a high-order cognitive task. Although large language models (LLMs) have shown impressive performance on many downstream NLP tasks, growing evide…
cs.AI2024★ 1 cited
WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive Psychologist
Chenyu Ren, Yazhou Zhang, Daihai He +1
Large language models (LLMs) are raging over the medical domain, and their momentum has carried over into the mental health domain, leading to the emergence of few mental health LL…