Self-improving data synthesis enables scalable adaptation of embedding models
cs.IR, cs.AI, cs.LG
Submitted: 2026-10-10
Updated: 2026-10-15
Code: https://github.com/zucanlv/SYNTRA
Sources
- InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
- Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels
- Synthetic Cross-language Information Retrieval Training Data
- InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval
- Gecko: Versatile Text Embeddings Distilled from Large Language Models
- InPars+: Supercharging Synthetic Data Generation for Information Retrieval Systems
- Text Embeddings by Weakly-Supervised Contrastive Pre-training
- Qwen3 Technical Report
- Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
- ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning
- NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMs
- The Faiss library
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