Understanding the Influence of Synthetic Data for Text Embedders
Bibliographic record
Abstract
Recent progress in developing general purpose text embedders has been driven by training on ever-growing corpora of synthetic LLMgenerated data.Nonetheless, no publicly available synthetic dataset exists, posing a barrier to studying its role for generalization.To address this issue, we first reproduce and publicly release the synthetic data proposed by Wang et al. ( 2024) (Mistral-E5).Our synthetic data is high quality and leads to consistent improvements in performance.Next, we critically examine where exactly synthetic data improves model generalization.Our analysis reveals that benefits from synthetic data are sparse and highly localized to individual datasets.Moreover, we observe trade-offs between the performance on different categories and data that benefits one task, degrades performance on another.Our findings highlight the limitations of current synthetic data approaches for building generalpurpose embedders and challenge the notion that training on synthetic data leads to more robust embedding models across tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".