2 citations · 5 across the 14 of their papers we have counts for
15 papers
When Models Hear What They Expect: Diagnosing Prosodic Heuristics in Multimodal Sarcasm Detection
Yongjian Chen, Pengfei Wei, Yiqun Sun +2
Multimodal Large Language Models (MLLMs) process speech and text jointly, yet whether they exploit prosodic cues for pragmatic inference or rely on surface acoustic patterns has re…
GGSS: Geodesic-Gated Spherical Steering for Inference-Time Debiasing of Generative Vision-Language Models
Yiqun Sun, Junyu Chen, Pengfei Wei +1
Generative vision-language models (VLMs) are increasingly used in human-centered settings, yet they can produce demographically biased outputs even when images differ only in contr…
DeepConnect: A Visual Analytics System for Bridging Interdisciplinary Research Collaborations
Yingchaojie Feng, Zekai Shao, Yiqun Sun +2
Interdisciplinary research collaboration is crucial for scientific innovation, but it remains difficult to initiate in practice. Existing collaborator discovery approaches are ofte…
Return of Frustratingly Easy Unsupervised Video Domain Adaptation
Pengfei Wei, Yiqun Sun, Zhiqiang Xu +2
Unsupervised video domain adaptation (UVDA) is a practical but under-explored problem. In this paper, we propose a frustratingly easy UVDA method, called MetaTrans. Specifically, M…
Very Efficient Listwise Multimodal Reranking for Long Documents
Yiqun Sun, Pengfei Wei, Lawrence B. Hsieh
Listwise reranking is a key yet computationally expensive component in vision-centric retrieval and multimodal retrieval-augmented generation (M-RAG) over long documents. While rec…
Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG
Yiqun Sun, Pengfei Wei, Lawrence B. Hsieh
Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results, with no view of how the corpus is orga…
OrchMAS: Orchestrated Reasoning with Multi Collaborative Heterogeneous Scientific Expert Structured Agents
Yichao Feng, Haoran Luo, Zhenghong Lin +4
Multi-agent large language model frameworks are promising for complex multi step reasoning, yet existing systems remain weak for scientific and knowledge intensive domains due to s…
One Swallow Does Not Make a Summer: Understanding Semantic Structures in Embedding Spaces
Yandong Sun, Qiang Huang, Ziwei Xu +3
Embedding spaces are fundamental to modern AI, translating raw data into high-dimensional vectors that encode rich semantic relationships. Yet, their internal structures remain opa…
Partially Shared Concept Bottleneck Models
Delong Zhao, Qiang Huang, Di Yan +2
Concept Bottleneck Models (CBMs) enhance interpretability by introducing a layer of human-understandable concepts between inputs and predictions. While recent methods automate conc…
Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval
Yixuan Tang, Yuanyuan Shi, Yiqun Sun +1
Access to diverse perspectives is essential for understanding real-world events, yet most news retrieval systems prioritize textual relevance, leading to redundant results and limi…
Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning
Yiqun Sun, Qiang Huang, Anthony K. H. Tung +1
This position paper argues that text embedding research should move beyond surface meaning and embrace implicit semantics as a central modeling objective. Text embeddings are a fou…
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation
Yingchaojie Feng, Yiqun Sun, Yandong Sun +4
In this work, we investigate an important task named instruction-following text embedding, which generates dynamic text embeddings that adapt to user instructions, highlighting spe…
PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder
Yiqun Sun, Qiang Huang, Anthony K. H. Tung +1
Semantic Text Embedding is a fundamental NLP task that encodes textual content into vector representations, where proximity in the embedding space reflects semantic similarity. Whi…
A General Framework for Producing Interpretable Semantic Text Embeddings
Yiqun Sun, Qiang Huang, Yixuan Tang +2
Semantic text embedding is essential to many tasks in Natural Language Processing (NLP). While black-box models are capable of generating high-quality embeddings, their lack of int…
Diversity-Aware -Maximum Inner Product Search Revisited
Qiang Huang, Yanhao Wang, Yiqun Sun +1
The -Maximum Inner Product Search (MIPS) serves as a foundational component in recommender systems and various data mining tasks. However, while most existing MIPS approac…