2 citations · 6 across the 39 of their papers we have counts for
21 papers · 1 filter
Attention Grounded Enhancement for Visual Document Retrieval
Wanqing Cui, Wei Huang, Yazhi Guo +4
Visual document retrieval requires understanding heterogeneous and multi-modal content to satisfy implicit information needs. Recent advances use screenshot-based document encoding…
Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding
Da Li, Yuxiao Luo, Keping Bi +7
Multimodal Large Language Models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enhancing performance across a rang…
Can LLM Annotations Replace User Clicks for Learning to Rank?
Lulu Yu, Keping Bi, Jiafeng Guo +4
Large-scale supervised data is essential for training modern ranking models, but obtaining high-quality human annotations is costly. Click data has been widely used as a low-cost a…
Annotation-Efficient Universal Honesty Alignment
Shiyu Ni, Keping Bi, Jiafeng Guo +4
Honesty alignment-the ability of large language models (LLMs) to recognize their knowledge boundaries and express calibrated confidence-is essential for trustworthy deployment. Exi…
Understanding Parametric Knowledge Injection in Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jingtong Wu +2
Context-grounded generation underpins many LLM applications, including long-document question answering (QA), conversational personalization, and retrieval-augmented generation (RA…
LLM-Specific Utility for Retrieval-Augmented Generation
Hengran Zhang, Keping Bi, Jiafeng Guo +4
Retrieval-augmented generation (RAG) is typically optimized for topical relevance, yet its success ultimately depends on whether retrieved passages are useful for a large language…
Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs
Zhikai Ding, Shiyu Ni, Keping Bi
Large vision-language models (LVLMs) demonstrate strong visual question answering (VQA) capabilities but are shown to hallucinate. A reliable model should perceive its knowledge bo…
How Do LLM-Generated Texts Impact Term-Based Retrieval Models?
Wei Huang, Keping Bi, Yinqiong Cai +3
As more content generated by large language models (LLMs) floods into the Internet, information retrieval (IR) systems now face the challenge of distinguishing and handling a blend…
A Comparative Study of Specialized LLMs as Dense Retrievers
Hengran Zhang, Keping Bi, Jiafeng Guo
While large language models (LLMs) are increasingly deployed as dense retrievers, the impact of their domain-specific specialization on retrieval effectiveness remains underexplore…
Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jiafeng Guo +1
Retrieval-augmented generation (RAG) has been widely adopted to augment large language models (LLMs) with external knowledge for knowledge-intensive tasks. However, its effectivene…
Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generation
Hengran Zhang, Keping Bi, Jiafeng Guo +4
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating retrieved information. Standard retrieval process prioritized relevance, focusing on top…
LifeIR at the NTCIR-18 Lifelog-6 Task
Jiahan Chen, Da Li, Keping Bi
In recent years, sharing lifelogs recorded through wearable devices such as sports watches and GoPros, has gained significant popularity. Lifelogs involve various types of informat…
Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity
Shiyu Ni, Keping Bi, Jiafeng Guo +1
Large language models (LLMs) often produce incorrect answers with high confidence, yet the factors associated with such overconfidence remain insufficiently understood. We study th…
Bridging Queries and Tables through Entities in Table Retrieval
Da Li, Keping Bi, Jiafeng Guo +1
Table retrieval is essential for accessing information stored in structured tabular formats; however, it remains less explored than text retrieval. The content of the table primari…
Utility-Focused LLM Annotation for Retrieval and Retrieval-Augmented Generation
Hengran Zhang, Minghao Tang, Keping Bi +5
This paper explores the use of large language models (LLMs) for annotating document utility in training retrieval and retrieval-augmented generation (RAG) systems, aiming to reduce…
Bacon-Shor Board Games
M. Sohaib Alam, Jun Zen, Thomas R. Scruby
We identify a period-4 measurement schedule for the checks of the Bacon-Shor code that fully covers spacetime with constant-weight detectors, and is numerically observed to provide…
Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective
Da Li, Keping Bi, Jiafeng Guo +1
Table retrieval, essential for accessing information through tabular data, is less explored compared to text retrieval. The row/column structure and distinct fields of tables (incl…
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance
Lulu Yu, Keping Bi, Jiafeng Guo +3
Most existing unbiased learning-to-rank (ULTR) approaches are based on the user examination hypothesis, which assumes that users will click a result only if it is both relevant and…
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification
Mingkun Zhang, Keping Bi, Wei Chen +2
In this paper, we aim to build an adversarially robust zero-shot image classifier. We ground our work on CLIP, a vision-language pre-trained encoder model that can perform zero-sho…
Estimating Commonsense Plausibility through Semantic Shifts
Wanqing Cui, Wei Huang, Keping Bi +2
Commonsense plausibility estimation is critical for evaluating language models (LMs), yet existing generative approaches--reliant on likelihoods or verbalized judgments--struggle w…
Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception
Shiyu Ni, Keping Bi, Jiafeng Guo +3
Large language models (LLMs) exhibit impressive performance across diverse tasks but often struggle to accurately gauge their knowledge boundaries, leading to confident yet incorre…