117 citations · 529 across the 70 of their papers we have counts for
15 papers · 1 filter
Thinking Forward and Backward: Multi-Objective Reinforcement Learning for Retrieval-Augmented Reasoning
Wenda Wei, Yu-An Liu, Ruqing Zhang +6
Retrieval-augmented generation (RAG) has proven to be effective in mitigating hallucinations in large language models, yet its effectiveness remains limited in complex, multi-step…
C2T-ID: Converting Semantic Codebooks to Textual Document Identifiers for Generative Search
Yingchen Zhang, Ruqing Zhang, Jiafeng Guo +4
Designing document identifiers (docids) that carry rich semantic information while maintaining tractable search spaces is a important challenge in generative retrieval (GR). Popula…
LLMs as Sparse Retrievers:A Framework for First-Stage Product Search
Hongru Song, Yu-an Liu, Ruqing Zhang +6
Product search is a crucial component of modern e-commerce platforms, with billions of user queries every day. In product search systems, first-stage retrieval should achieve high…
A Generative Framework for Personalized Sticker Retrieval
Changjiang Zhou, Ruqing Zhang, Jiafeng Guo +4
Formulating information retrieval as a variant of generative modeling, specifically using autoregressive models to generate relevant identifiers for a given query, has recently att…
Retrieval-in-the-Chain: Bootstrapping Large Language Models for Generative Retrieval
Yingchen Zhang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval (GR) is an emerging paradigm that leverages large language models (LLMs) to autoregressively generate document identifiers (docids) relevant to a given query.…
VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
Haosheng Qian, Yixing Fan, Jiafeng Guo +4
Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impr…
Does Generative Retrieval Overcome the Limitations of Dense Retrieval?
Yingchen Zhang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval (GR) has emerged as a new paradigm in neural information retrieval, offering an alternative to dense retrieval (DR) by directly generating identifiers of relev…
Reason to Retrieve: Enhancing Query Understanding through Decomposition and Interpretation
Yunfei Zhong, Jun Yang, Yixing Fan +4
Query understanding (QU) aims to accurately infer user intent to improve document retrieval. It plays a vital role in modern search engines. While large language models (LLMs) have…
On the Scaling of Robustness and Effectiveness in Dense Retrieval
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Robustness and Effectiveness are critical aspects of developing dense retrieval models for real-world applications. It is known that there is a trade-off between the two. Recent wo…
The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems
Hongru Song, Yu-an Liu, Ruqing Zhang +4
We explore adversarial attacks against retrieval-augmented generation (RAG) systems to identify their vulnerabilities. We focus on generating human-imperceptible adversarial exampl…
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems
Hongru Song, Yu-an Liu, Ruqing Zhang +2
Retrieval-augmented generation (RAG) systems can effectively mitigate the hallucination problem of large language models (LLMs),but they also possess inherent vulnerabilities. Iden…
An Empirical Study of Evaluating Long-form Question Answering
Ning Xian, Yixing Fan, Ruqing Zhang +2
\Ac{LFQA} aims to generate lengthy answers to complex questions. This scenario presents great flexibility as well as significant challenges for evaluation. Most evaluations rely on…
Robust-IR @ SIGIR 2025: The First Workshop on Robust Information Retrieval
Yu-An Liu, Haya Nachimovsky, Ruqing Zhang +3
With the advancement of information retrieval (IR) technologies, robustness is increasingly attracting attention. When deploying technology into practice, we consider not only its…
TrustRAG: An Information Assistant with Retrieval Augmented Generation
Yixing Fan, Qiang Yan, Wenshan Wang +3
\Ac{RAG} has emerged as a crucial technique for enhancing large models with real-time and domain-specific knowledge. While numerous improvements and open-source tools have been pro…
Generative Retrieval for Book search
Yubao Tang, Ruqing Zhang, Jiafeng Guo +5
In book search, relevant book information should be returned in response to a query. Books contain complex, multi-faceted information such as metadata, outlines, and main text, whe…