4 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…
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…
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…