2 citations · 5 across the 14 of their papers we have counts for
6 papers · 1 filter
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…