8 citations · 13 across the 13 of their papers we have counts for
6 papers · 1 filter
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data
Juanhui Li, Sreyashi Nag, Hui Liu +7
In real-world NLP applications, Large Language Models (LLMs) offer promising solutions due to their extensive training on vast datasets. However, the large size and high computatio…
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
Ran Xu, Hui Liu, Sreyashi Nag +8
Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge. However, adapting general-pur…
Exploring Query Understanding for Amazon Product Search
Chen Luo, Xianfeng Tang, Hanqing Lu +12
Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique chara…
REAPER: Reasoning based Retrieval Planning for Complex RAG Systems
Ashutosh Joshi, Sheikh Muhammad Sarwar, Samarth Varshney +3
Complex dialog systems often use retrieved evidence to facilitate factual responses. Such RAG (Retrieval Augmented Generation) systems retrieve from massive heterogeneous data stor…
IterAlign: Iterative Constitutional Alignment of Large Language Models
Xiusi Chen, Hongzhi Wen, Sreyashi Nag +5
With the rapid development of large language models (LLMs), aligning LLMs with human values and societal norms to ensure their reliability and safety has become crucial. Reinforcem…
Hierarchical Query Classification in E-commerce Search
Bing He, Sreyashi Nag, Limeng Cui +5
E-commerce platforms typically store and structure product information and search data in a hierarchy. Efficiently categorizing user search queries into a similar hierarchical stru…