Papers with Gaussian Processes
Assessing Quality Estimation Models for Sentence-Level Prediction (C18-1)
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| Challenge: | Using a relevant QE model is also very important in QE. |
| Approach: | They evaluate a wide range of advanced sentence-level Quality Estimation models including Support Vector Regression, Ride Regression and Bayesian Neural Networks. |
| Outcome: | The proposed models behave differently in evaluation settings depending on whether test data come from the same domain as the training data or not. |
Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval (2026.findings-acl)
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Junyoung Kim, Anton Korikov, Jiazhou Liang, Justin Cui, Yifan Simon Liu, Qianfeng Wen, Mark Zhao, Scott Sanner
| Challenge: | Existing approaches passively rely on first-stage dense retrievers, which leads to two limitations: failing to retrieve relevant passages in semantically distinct clusters and failing to propagate relevance signals to the broader corpus. |
| Approach: | They propose a framework that propagates sparse LLM relevance signals across the embedding space to guide global exploration. |
| Outcome: | Experiments show that the proposed framework outperforms existing approaches under the same budget on all four datasets. |