117 citations · 414 across the 107 of their papers we have counts for
17 papers · 1 filter
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…
HEC-GCN: Hypergraph Enhanced Cascading Graph Convolution Network for Multi-Behavior Recommendation
Yabo Yin, Xiaofei Zhu, Wenshan Wang +4
Multi-behavior recommendation (MBR) has garnered growing attention recently due to its ability to mitigate the sparsity issue by inferring user preferences from various auxiliary b…
A Claim Decomposition Benchmark for Long-form Answer Verification
Zhihao Zhang, Yixing Fan, Ruqing Zhang +1
The advancement of LLMs has significantly boosted the performance of complex long-form question answering tasks. However, one prominent issue of LLMs is the generated "hallucinatio…
On the Capacity of Citation Generation by Large Language Models
Haosheng Qian, Yixing Fan, Ruqing Zhang +1
Retrieval-augmented generation (RAG) appears as a promising method to alleviate the "hallucination" problem in large language models (LLMs), since it can incorporate external trace…
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense
Mingkun Zhang, Keping Bi, Wei Chen +3
Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated…
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs
Sihui Yang, Keping Bi, Wanqing Cui +2
Non-Factoid (NF) Question Answering (QA) is challenging to evaluate due to diverse potential answers and no objective criterion. The commonly used automatic evaluation metrics like…
Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method
Weichao Zhang, Ruqing Zhang, Jiafeng Guo +3
As the scale of training corpora for large language models (LLMs) grows, model developers become increasingly reluctant to disclose details on their data. This lack of transparency…
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank
Lulu Yu, Keping Bi, Shiyu Ni +1
Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods…
Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?
Shiyu Ni, Keping Bi, Lulu Yu +1
Large language models (LLMs) have been found to produce hallucinations when the question exceeds their internal knowledge boundaries. A reliable model should have a clear perceptio…
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…
QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory
Yihang Wang, Xu Huang, Bowen Tian +6
Generative LLM have achieved remarkable success in various industrial applications, owing to their promising In-Context Learning capabilities. However, the issue of long context in…
Look Globally and Reason: Two-stage Path Reasoning over Sparse Knowledge Graphs
Saiping Guan, Jiyao Wei, Xiaolong Jin +2
Sparse Knowledge Graphs (KGs), frequently encountered in real-world applications, contain fewer facts in the form of (head entity, relation, tail entity) compared to more populated…
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…
Temporal Knowledge Graph Question Answering: A Survey
Miao Su, Zixuan Li, Zhuo Chen +3
Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a…
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction
Zixuan Li, Yutao Zeng, Yuxin Zuo +14
In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal Information Extraction (UIE) via code generation. KnowCoder aims to develop a kind of unified…
CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
Jiafeng Guo, Changjiang Zhou, Ruqing Zhang +4
Knowledge-intensive language tasks (KILTs) typically require retrieving relevant documents from trustworthy corpora, e.g., Wikipedia, to produce specific answers. Very recently, a…