1 citations · 1 across the 5 of their papers we have counts for
5 papers
Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs
Shenglai Zeng, Tianqi Zheng, Chuan Tian +10
Personalizing large language models (LLMs) to individual users requires incorporating extensive interaction histories and profiles, but input token constraints make this impractica…
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
Shenglai Zeng, Jiankun Zhang, Bingheng Li +8
Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively…
AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset
Dante Everaert, Rohit Patki, Tianqi Zheng +1
Query Autocomplete (QAC) is a critical feature in modern search engines, facilitating user interaction by predicting search queries based on input prefixes. Despite its widespread…
Retrieval Augmented Spelling Correction for E-Commerce Applications
Xuan Guo, Rohit Patki, Dante Everaert +1
The rapid introduction of new brand names into everyday language poses a unique challenge for e-commerce spelling correction services, which must distinguish genuine misspellings f…
GIO: Gradient Information Optimization for Training Dataset Selection
Dante Everaert, Christopher Potts
It is often advantageous to train models on a subset of the available train examples, because the examples are of variable quality or because one would like to train with fewer exa…