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GTR: scaling dual encoders while holding embedding size fixed

Contribution grade

Generalizable dense retrieval · Human-designed research method or resource

Reference score115,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Studied larger T5-derived dual encoders with a fixed768-dimensional output, combining web-pair pretraining and labeled retrieval fine-tuning to test out-of-domain retrieval generalization.

Scientific scope

Studied larger T5-derived dual encoders with a fixed768-dimensional output, combining web-pair pretraining and labeled retrieval fine-tuning to test out-of-domain retrieval generalization. Source reports a controlled scaling and training study, not invention of T5 or dual encoders. Fixed embedding dimension does not fix encoder compute or training-data cost. Findings on BEIR and supervised-data subsamples do not prove universal scaling or domain generalization; no biological outcome is added.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Source reports a controlled scaling and training study, not invention of T5 or dual encoders. Fixed embedding dimension does not fix encoder compute or training-data cost. Findings on BEIR and supervised-data subsamples do not prove universal scaling or domain generalization; no biological outcome is added.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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