most citedA General Framework for Producing Interpretable Semantic Text Embeddings

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI20252 cited

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…

cs.CL2025

MPCG: Multi-Round Persona-Conditioned Generation for Modeling the Evolution of Misinformation with LLMs

Jun Rong Brian Chong, Yixuan Tang, Anthony K. H. Tung

Misinformation evolves as it spreads, shifting in language, framing, and moral emphasis to adapt to new audiences. However, current misinformation detection approaches implicitly a…

cs.CL2025

The Missing Parts: Augmenting Fact Verification with Half-Truth Detection

Yixuan Tang, Jincheng Wang, Anthony K. H. Tung

Fact verification systems typically assess whether a claim is supported by retrieved evidence, assuming that truthfulness depends solely on what is stated. However, many real-world…

cs.CL2025

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…

cs.CL20242 cited

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…