most citedMoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property

3 citations · 3 across the 7 of their papers we have counts for

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cs.CL2026

SICI: A Semantic-Pragmatic Complexity Index Reveals Regime Shifts in LLM Stance Detection

Fuqiang Niu, Bowen Zhang

Prompt-based LLMs are increasingly used for stance detection, but harder examples are not always repaired by clearer instructions, reasoning prompts, retrieval, or debate. We intro…

cs.CL2026

A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit

Hu Huang, Genan Dai, Fuqiang Niu +3

Bioethical debates increasingly unfold on social media, yet stance detection research lacks large-scale, domain-specific resources for modeling such context-dependent discourse. We…

cs.CL2024

DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms

Fuqiang Niu, Minghuan Tan, Bowen Zhang +2

Idioms represent a ubiquitous vehicle for conveying sentiments in the realm of everyday discourse, rendering the nuanced analysis of idiom sentiment crucial for a comprehensive und…

cs.CL2024

A Survey of Stance Detection on Social Media: New Directions and Perspectives

Bowen Zhang, Genan Dai, Fuqiang Niu +5

In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these…

cs.CL2024

MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models

Zebang Cheng, Fuqiang Niu, Yuxiang Lin +3

This paper presents our winning submission to Subtask 2 of SemEval 2024 Task 3 on multimodal emotion cause analysis in conversations. We propose a novel Multimodal Emotion Recognit…

cs.CL20243 cited

MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property

Shiwen Ni, Minghuan Tan, Yuelin Bai +9

Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs p…