From the 1 of 16 linked papers with an AI index.
2 citations · 2 across the 7 of their papers we have counts for
9 papers · 1 filter
LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval
Jiajie Jin, Yanzhao Zhang, Mingxin Li +4
LLMs have fundamentally transformed dense retrieval, upgrading backbones from discriminative encoders to generative architectures. However, a critical disconnect remains: while LLM…
Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
Mingxin Li, Yanzhao Zhang, Dingkun Long +9
In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, t…
E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search
Qi Liu, Yanzhao Zhang, Mingxin Li +3
Text embedding models deliver competitive retrieval performance with high efficiency, but their ranking fidelity remains limited compared to LLM-based listwise rerankers, which cap…
When Text Embedding Meets Large Language Model: A Comprehensive Survey
Zhijie Nie, Zhangchi Feng, Mingxin Li +4
Text embedding has become a foundational technology in natural language processing (NLP) during the deep learning era, driving advancements across a wide array of downstream tasks.…
Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking
Ziqi Dai, Xin Zhang, Mingxin Li +6
In information retrieval, training reranking models mainly focuses on two types of objectives: metric learning (e.g. contrastive loss to increase the predicted scores on relevant q…
Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
Yanzhao Zhang, Mingxin Li, Dingkun Long +9
In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon…