1 citations · 1 across the 4 of their papers we have counts for
6 papers
Preserving rare-item recall under a storage budget: quantization, distillation, and the limits of teacher fidelity
Han Chen, Yingrui Li
Under a fixed embedding-storage budget, recommenders can use a small full-precision model, a distilled student, or a higher-rank low-precision model. We asked which choice preserve…
Candidate-dependent residual routing trades top-of-list accuracy for recall in sequential recommendation
Han Chen, Yingrui Li
Candidate-dependent routing lets a sequential recommender weight a user's past behaviors differently for each item it scores. We test whether this flexibility helps by adding a sma…
STARS: Semantic Tokens with Augmented Representations for Recommendation at Scale
Han Chen, Steven Zhu, Yingrui Li
Real-world ecommerce recommender systems must deliver relevant items under strict tens-of-milliseconds latency constraints despite challenges such as cold-start products, rapidly s…
LogQuant: Log-Distributed 2-Bit Quantization of KV Cache with Superior Accuracy Preservation
Han Chen, Zicong Jiang, Zining Zhang +4
We introduce LogQuant, a groundbreaking 2-bit quantization technique for KV Cache in large language model (LLM) inference, delivering substantial memory savings while preserving su…
Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models
Wenqi Pei, Hailing Xu, Hengyuan Zhao +5
Natural Language to SQL (NL2SQL) has seen significant advancements with large language models (LLMs). However, these models often depend on closed-source systems and high computati…
No More Distractions: an Adaptive Up-Sampling Algorithm to Reduce Data Artifacts
Han Chen
Researchers recently found out that sometimes language models achieve high accuracy on benchmark data set, but they can not generalize very well with even little changes to the ori…