7 papers
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models
Youan Cong, Pritom Saha Akash, Cheng Wang +1
We introduce the \textit{Extract-Refine-Retrieve-Read} (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RA…
Cascade Speculative Drafting for Even Faster LLM Inference
Ziyi Chen, Xiaocong Yang, Jiacheng Lin +3
Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft. A larger target model then r…
RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems
Duy A. Nguyen, Rishi Kesav Mohan, Van Yang +2
Query rewriting (QR) is a critical technique in e-commerce search, addressing the lexical gap between user queries and product descriptions to enhance search performance. Existing…
Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling
Pritom Saha Akash, Kevin Chen-Chuan Chang
Topic modeling plays a vital role in uncovering hidden semantic structures within text corpora, but existing models struggle in low-resource settings where limited target-domain da…
ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation
Lam Thanh Do, Aaditya Bodke, Pritom Saha Akash +1
Unsupervised keyphrase prediction has gained growing interest in recent years. However, existing methods typically rely on heuristically defined importance scores, which may lead t…
ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation
Kashob Kumar Roy, Pritom Saha Akash, Kevin Chen-Chuan Chang +1
Open-domain long-form text generation requires generating coherent, comprehensive responses that address complex queries with both breadth and depth. This task is challenging due t…