7 citations · 29 across the 54 of their papers we have counts for
9 papers · 1 filter
Learning to Retrieve from Agent Trajectories
Yuqi Zhou, Sunhao Dai, Changle Qu +3
Information retrieval (IR) systems have traditionally been designed and trained for human users, with learning-to-rank methods relying heavily on large-scale human interaction logs…
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos
Haowen Gao, Liang Pang, Shicheng Xu +4
With the rapid development of AI-generated content (AIGC), the creation of high-quality AI-generated videos has become faster and easier, resulting in the Internet being flooded wi…
List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation
Shicheng Xu, Liang Pang, Jun Xu +2
The results of information retrieval (IR) are usually presented in the form of a ranked list of candidate documents, such as web search for humans and retrieval-augmented generatio…
Plot Retrieval as an Assessment of Abstract Semantic Association
Shicheng Xu, Liang Pang, Jiangnan Li +5
Retrieving relevant plots from the book for a query is a critical task, which can improve the reading experience and efficiency of readers. Readers usually only give an abstract an…
Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images
Shicheng Xu, Danyang Hou, Liang Pang +4
With the advancement of generation models, AI-generated content (AIGC) is becoming more realistic, flooding the Internet. A recent study suggests that this phenomenon causes source…
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval
Shicheng Xu, Liang Pang, Huawei Shen +1
Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets. However, previous studies have found that dense retrieval is hard…
NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework
Shicheng Xu, Liang Pang, Huawei Shen +1
Information retrieval aims to find information that meets users' needs from the corpus. Different needs correspond to different IR tasks such as document retrieval, open-domain que…
LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback
Yunchang Zhu, Liang Pang, Yanyan Lan +2
Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressio…
L2R2: Leveraging Ranking for Abductive Reasoning
Yunchang Zhu, Liang Pang, Yanyan Lan +1
The abductive natural language inference task (NLI) is proposed to evaluate the abductive reasoning ability of a learning system. In the NLI task, two observations are given…