3 papers
cs.IR2024
Session Context Embedding for Intent Understanding in Product Search
Navid Mehrdad, Vishal Rathi, Sravanthi Rajanala
It is often noted that single query-item pair relevance training in search does not capture the customer intent. User intent can be better deduced from a series of engagements (Cli…
cs.CL2024
Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models
Xuyang Wu, Zhiyuan Peng, Krishna Sravanthi Rajanala Sai +2
Effective passage retrieval and reranking methods have been widely utilized to identify suitable candidates in open-domain question answering tasks, recent studies have resorted to…
cs.CL2024
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
Zhiyuan Peng, Xuyang Wu, Qifan Wang +2
Parameter Efficient Fine-Tuning (PEFT) methods have been extensively utilized in Large Language Models (LLMs) to improve the down-streaming tasks without the cost of fine-tuing the…