HARNESS-LM uses teacher fine-tuning, L2 query alignment, and contrastive refinement to distill large SLM retrievers into compact models that recover 98% precision with up to 27x lower latency on Bing Ads benchmarks.
Nv-embed: Improved techniques for training llms as generalist embedding models
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
HARNESS-LM: A Three-Phase Training Recipe for Harnessing SLMs in Sponsored Search Retrieval
HARNESS-LM uses teacher fine-tuning, L2 query alignment, and contrastive refinement to distill large SLM retrievers into compact models that recover 98% precision with up to 27x lower latency on Bing Ads benchmarks.