5 papers · 1 filter
Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval
Junyoung Kim, Anton Korikov, Jiazhou Liang +5
While Large Language Models (LLMs) exhibit exceptional zero-shot relevance modeling, their high computational cost necessitates framing passage retrieval as a budget-constrained gl…
Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation
Jiazhou Liang, Yifan Simon Liu, David Guo +3
The growing ubiquity of Extended Reality (XR) is driving Conversational Recommendation Systems (CRS) toward visually immersive experiences. We formalize this paradigm as Immersive…
Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments
Yifan Liu, Qianfeng Wen, Jiazhou Liang +6
Natural Language Recommendation (NLRec) generates item suggestions based on the relevance between user-issued NL requests and NL item description passages. Existing NLRec approache…
VOGUE: A Multimodal Dataset for Conversational Recommendation in Fashion
David Guo, Minqi Sun, Yilun Jiang +2
Multimodal conversational recommendation has recently emerged as a promising paradigm for delivering personalized experiences through natural dialogue enriched by visual and contex…
MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval
Yifan Liu, Qianfeng Wen, Mark Zhao +2
Dense Passage Retrieval (DPR) typically relies on Euclidean or cosine distance to measure query-passage relevance in embedding space, which is effective when embeddings lie on a li…