Papers by Qianfeng Wen
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. |
MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval (2025.emnlp-main)
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| Challenge: | Empirical evidence suggests that manifold-aware distance allows DPR to leverage context from related neighboring passages. |
| Approach: | They propose a "manifold-aware" distance metric that measures query–passage distance . they propose to model the intrinsic manifold structure of passages using a nearest-neighbor graph . |
| Outcome: | Empirical evidence suggests MA-DPR outperforms Euclidean and cosine distances on OOD . it can be applied to a wide range of dense embedding and retrieval tasks . |
Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments (2026.findings-acl)
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Yifan Simon Liu, Qianfeng Wen, Jiazhou Liang, Mark Zhao, Justin Cui, Anton Korikov, Armin Toroghi, Junyoung Kim, Scott Sanner
| Challenge: | Existing NLRec approaches use Dense Retrieval to compute item relevance scores . DR views the request as the sole relevance label, leading to a weak proxy for query relevance. |
| Approach: | They propose to use Gaussian Process Regression to model item relevance . they propose to combine LLM with a Gauss-based kernel to model multimodal relevance judging . |
| Outcome: | The proposed approach outperforms simpler unimodal kernels and baseline methods by up to 65% on four NLRec datasets and two LLM backbones. |