2 citations · 3 across the 5 of their papers we have counts for
5 papers
IDRBench: Benchmarking the Interactive Capabilities of Deep Research Agents
Yingchaojie Feng, Qiang Huang, Xiaoya Xie +4
Large Language Model (LLM)-based deep research agents perform multi-step reasoning, web exploration, and long-form report generation. In these long-horizon workflows, early deviati…
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…
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…
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…