4 citations · 16 across the 27 of their papers we have counts for
8 papers · 1 filter
ResidualKV: Residual-Based KV Cache Compression for Efficient Long-Context Inference
Jitai Hao, Qiang Huang, Yaowei Wang +2
Efficient long-context inference faces two coupled bottlenecks: KV-cache memory grows linearly with context length, while attention computation grows quadratically. Existing approa…
When Abundance Conceals Weakness: Knowledge Conflict in Multilingual Models
Jiaqi Zhao, Qiang Huang, Haodong Chen +2
Large Language Models (LLMs) encode vast world knowledge across multiple languages, yet their internal beliefs are often unevenly distributed across linguistic spaces. When externa…
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…
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 Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone
Jitai Hao, Qiang Huang, Hao Liu +3
Training high-performing Small Language Models (SLMs) remains costly, even with knowledge distillation and pruning from larger teacher models. Existing work often faces three key c…
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…