README.md

---
license: cc-by-4.0
tags:
- finephrase
- diversity
- gvendi
- prismatic-synthesis
- correlation
- synthetic-data
size_categories:
- n<1K
---

# Use a base-model proxy for G-Vendi, and score it within format

Companion data for the FinePhrase Synthetic Data Playbook (Niklaus et al., 2026),
on the 83-cell grid from its Figure 22. G-Vendi (Jung et al., 2025) is a
gradient-space diversity metric that the playbook uses as one of several
predictors of downstream performance.

![Correlation of each predictor (rows) with each downstream benchmark (columns),
across the grid.](correlation_heatmap.png)

The heatmap correlates each predictor with each benchmark across the grid. The
top three blocks are the playbook's other predictors (DCLM, Edu, and the
embedding-based diversity metrics: Vendi Score, cosine similarity, near-duplicate
rate). The bottom block is G-Vendi, the gradient-based diversity metric, built up
one row at a time:

- **Published / Reproduced.** The first row is the playbook's published G-Vendi;
  the second is our re-run of the same pipeline from scratch. They agree
  (cell-level rank-correlation 0.95), so the reproduction is faithful.
- **Base model.** The third row swaps the proxy model from the instruction-tuned
  Qwen3-0.6B to its pretrained base, holding everything else fixed. G-Vendi
  backpropagates each document through a small proxy LLM; it was introduced for
  curating reasoning datasets, where an instruction-tuned proxy is the natural
  choice, and the playbook uses one too. We are evaluating **pretraining** data,
  so we match the proxy to the data and use the base model.
- **Base model, within-prompt.** The last row scores the base model within each
  format (z-scoring inside each `(bucket, prompt)` group before correlating).
  This strips out differences between formats, so it tests whether the metric
  picks the better rephraser for a given format.

In that final row the base-model proxy reaches **+0.57** on the macro average (p < 0.001).

Comparing that row against DCLM-difference, the strongest reference predictor:
DCLM-difference leads on the macro and micro averages, and math and table; the
base-model within-prompt row leads on general knowledge, and on reasoning and NLU,
where DCLM has little signal. On reading comprehension the two rows are tied, but
the base-model within-prompt row leads on SQuAD v2. Note the two rows are scored
differently: DCLM-difference is raw-pooled, while the base-model row is
within-prompt.

## Files and reproducing

```
python gvendi_correlations.py   # raw and within-prompt correlations
python make_heatmap.py          # writes correlation_heatmap.png
```

`data/` holds per-cell G-Vendi for the published pipeline (`pub.json`), our
reproduction (`repro/`), and the base-model swap (`prx06bb/`), plus the playbook's
predictor and downstream scores (`reference_predictors.json`,
`downstream_scores.json`), both taken from its `rephrasing_metadata.json` so the
raw rows reproduce Figure 22 exactly.

## Citation

```bibtex
@misc{puduppully2026gvendiproxy,
  author       = {Puduppully, Ratish},
  title        = {Use a Base-Model Proxy for G-Vendi, and Score It Within Format},
  year         = {2026},
  howpublished = {HuggingFace dataset},
  url          = {https://huggingface.co/datasets/ratishsp/g-vendi-base-proxy}
}
```

Please also cite the work this builds on: the FinePhrase Synthetic Data Playbook
(Niklaus et al., 2026), Prismatic Synthesis / G-Vendi (Jung et al., 2025,
arXiv:2505.20161), and the Vendi Score (Friedman & Dieng, 2023).