GLint: A Field Report on Hard Negatives for Late-Interaction Retrievers
antoine_chaffin · x · 2026-08-09
The author shares a detailed experimental report on building GLInt, a 150M parameter late-interaction retriever. The model achieves a 57.43 mean nDCG@10 across 15 BEIR tasks, setting a new SOTA for models under 300M parameters.
Key Research & Findings:
- Mining Space: Explores whether mining hard negatives in multi-vector MaxSim space yields better late-interaction models compared to traditional dense retriever mining.
- Hard vs. False Negatives: Analyzes the challenges of constructing hard negatives in multi-vector scenarios and how to filter effectively to avoid false negatives.
- Training Strategy: Transitions from Supervised Fine-Tuning (SFT) to knowledge distillation to boost performance further.
- Pitfalls: The article includes a dedicated section on experiments that did not work, demonstrating a complete research loop from hypothesis and diagnosis to iteration.
Related event: GLint: SOTA Pure Late-Interaction Retrieval Model(2 posts)→
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