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Self-improving data synthesis enables scalable adaptation of embedding models
Zucan Lyu, Rui Tang, Jianlyu Chen +3
AI agents require specialised retrieval across domains and tasks, but adapting embedding models is constrained by costly manual training-data design. Here we introduce SYNTRA, a se…
Retro*: Optimizing LLMs for Reasoning-Intensive Document Retrieval
Junwei Lan, Jianlyu Chen, Zheng Liu +3
With the growing popularity of LLM agents and RAG, it has become increasingly important to retrieve documents that are essential for solving a task, even when their connection to t…
ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval
Jianlyu Chen, Junwei Lan, Chaofan Li +2
In this paper, we introduce ReasonEmbed, a novel text embedding model developed for reasoning-intensive document retrieval. Our work includes three key technical contributions. Fir…
MR-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval
Junjie Zhou, Ze Liu, Lei Xiong +10
Multimodal retrieval is becoming a crucial component of modern AI applications, yet its evaluation lags behind the demands of more realistic and challenging scenarios. Existing ben…
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis
Chaofan Li, Jianlyu Chen, Yingxia Shao +2
Code embedding models attract increasing attention due to the widespread popularity of retrieval-augmented generation (RAG) in software development. These models are expected to ca…
AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark
Jianlyu Chen, Nan Wang, Chaofan Li +6
Evaluation plays a crucial role in the advancement of information retrieval (IR) models. However, current benchmarks, which are based on predefined domains and human-labeled data,…