3 citations · 6 across the 10 of their papers we have counts for
4 papers · 1 filter
SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
Xiaopeng Li, Xiangyang Li, Hao Zhang +6
The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling. Traditional DR methods primarily depend on naive negative sampling techniqu…
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation
Pengyue Jia, Derong Xu, Xiaopeng Li +9
The reranker and generator are two critical components in the Retrieval-Augmented Generation (i.e., RAG) pipeline, responsible for ranking relevant documents and generating respons…
SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
Pengyue Jia, Zhaocheng Du, Yichao Wang +6
Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Conventional approaches typically employ surrogate models-such as dec…
ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
Pengyue Jia, Yejing Wang, Zhaocheng Du +6
Deep Recommender Systems (DRS) are increasingly dependent on a large number of feature fields for more precise recommendations. Effective feature selection methods are consequently…