UWE is a task-agnostic bi-encoder that uses many-to-many InfoNCE and token-level soft late interaction to achieve zero-shot ranking across unseen work-related target spaces while using far fewer parameters than Qwen3-8B and improving MAP by 4.4 points.
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Pith papers citing it
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2025 2verdicts
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VoteGCL augments graph-based recommendation systems with high-confidence synthetic interactions generated via majority-voting LLM reranks and integrates them into graph contrastive learning to improve accuracy and reduce popularity bias.
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Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker
UWE is a task-agnostic bi-encoder that uses many-to-many InfoNCE and token-level soft late interaction to achieve zero-shot ranking across unseen work-related target spaces while using far fewer parameters than Qwen3-8B and improving MAP by 4.4 points.
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VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation
VoteGCL augments graph-based recommendation systems with high-confidence synthetic interactions generated via majority-voting LLM reranks and integrates them into graph contrastive learning to improve accuracy and reduce popularity bias.