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20222026
most citedBias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era

124 citations · 152 across the 22 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CV2024

Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models

Shicheng Xu, Liang Pang, Yunchang Zhu +2

Vision-language alignment in Large Vision-Language Models (LVLMs) successfully enables LLMs to understand visual input. However, we find that existing vision-language alignment met…

cs.CL2024

A Theory for Token-Level Harmonization in Retrieval-Augmented Generation

Shicheng Xu, Liang Pang, Huawei Shen +1

Retrieval-augmented generation (RAG) utilizes retrieved texts to enhance large language models (LLMs). Studies show that while RAG provides valuable external information (benefit),…

cs.IR2024★ 124 cited

Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era

Sunhao Dai, Chen Xu, Shicheng Xu +3

With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradi…

cs.IR2024

List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation

Shicheng Xu, Liang Pang, Jun Xu +2

The results of information retrieval (IR) are usually presented in the form of a ranked list of candidate documents, such as web search for humans and retrieval-augmented generatio…

cs.CL2024★ 1 cited

Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation

Shicheng Xu, Liang Pang, Mo Yu +4

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval. However, studies have shown that LLMs still face…