1 citations · 1 across the 10 of their papers we have counts for
10 papers
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…
ViFP: A Framework for Visual False Positive Detection to Enhance Reasoning Reliability in VLMs
Ben Zhang, LuLu Yu, Lei Gao +3
During reasoning in vision-language models (VLMs), false positive (FP) reasoning occurs when a model produces the correct answer but follows an incorrect reasoning path, resulting…
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…
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…
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…
CIR at the NTCIR-17 ULTRE-2 Task
Lulu Yu, Keping Bi, Jiafeng Guo +1
The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on the U…
Robust Multimodal Failure Detection for Microservice Systems
Chenyu Zhao, Minghua Ma, Zhenyu Zhong +10
Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's perform…
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search
Xiaojie Sun, Lulu Yu, Yiting Wang +2
An effective ranking model usually requires a large amount of training data to learn the relevance between documents and queries. User clicks are often used as training data since…
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank
Lulu Yu, Yiting Wang, Xiaojie Sun +2
Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker. In th…