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20242026
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cs.CL2026

LLMs + Persona-Plug = Personalized LLMs

Jiongnan Liu, Yutao Zhu, Shuting Wang +6

Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual inter…

cs.CL2025

Large Language Models for Information Retrieval: A Survey

Yutao Zhu, Huaying Yuan, Shuting Wang +7

As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve…

cs.CL2025

OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain

Shuting Wang, Jiejun Tan, Zhicheng Dou +1

As a typical and practical application of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) techniques have gained extensive attention, particularly in vertical do…

cs.CL2024

RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation

Shuting Wang, Xin Yu, Mang Wang +3

Retrieval-augmented generation (RAG) effectively addresses issues of static knowledge and hallucination in large language models. Existing studies mostly focus on question scenario…

cs.CL2024

DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation

Shuting Wang, Jiongnan Liu, Shiren Song +6

Retrieval-Augmented Generation (RAG) offers a promising solution to address various limitations of Large Language Models (LLMs), such as hallucination and difficulties in keeping u…