117 citations · 529 across the 70 of their papers we have counts for
52 papers · 1 filter
Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation
Yunfei Zhong, Jun Yang, Wei Huang +7
A deployable multilingual reranker must not only generalize across languages, domains, and ranking tasks, but also remain efficient to serve as a second-stage reranker in practical…
AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning
Hongru Song, Yu-An Liu, Ruqing Zhang +4
Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack surfaces. We study knowledge-base…
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…
Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks Against Black-box Neural Ranking Models
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Neural ranking models (NRMs) have been shown to be highly effective in terms of retrieval performance. Unfortunately, they have also displayed a higher degree of sensitivity to att…
On the Robustness of Generative Information Retrieval Models
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Generative information retrieval methods retrieve documents by directly generating their identifiers. Much effort has been devoted to developing effective generative IR models. Les…
Generative Retrieval Meets Multi-Graded Relevance
Yubao Tang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval represents a novel approach to information retrieval. It uses an encoder-decoder architecture to directly produce relevant document identifiers (docids) for qu…
Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ens…
Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval
Yubao Tang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval uses differentiable search indexes to directly generate relevant document identifiers in response to a query. Recent studies have highlighted the potential of…
Robust Information Retrieval
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +1
Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only d…
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Adversarial ranking attacks have gained increasing attention due to their success in probing vulnerabilities, and, hence, enhancing the robustness, of neural ranking models. Conven…
Listwise Generative Retrieval Models via a Sequential Learning Process
Yubao Tang, Ruqing Zhang, Jiafeng Guo +3
Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identi…