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license: other
tags:
- uncertainty-quantification
- diffusion-llm
- lm-polygraph
---
# LLaDA 2.1 mini — UQ eval pickles (`ue_manager_seed1`)
Durable backup of the LLaDA 2.1 mini uncertainty-quantification sweep pickles
produced with `lm-polygraph` (branch `feat/llada2-cache`, April–May 2026).
Each `.pkl` is a `torch.load`-able `ue_manager` dump containing per-sample
`stats` (greedy_texts, target_texts, ...), `estimations`, `gen_metrics`, and
`metrics`.
## Layout
```
setup_a/ no-train baselines, reduced config (~34 est, K=10 sampling dropped)
setup_b/ with-train baselines (79 est)
setup_c/ train-only estimators (6 est: TAD/MIND/SAPLMA/SATRMD/LookBackLens/Mahalanobis)
setup_d/ diffusion methods, merged config (542 est incl. ConfidenceAtCommit family)
```
## Coverage (8 datasets in scope + truthfulqa)
| Dataset | setup_a | setup_b | setup_c | setup_d |
|---|:-:|:-:|:-:|:-:|
| coqa | — | — | — | ✅ |
| gsm8k | — | — | — | ✅ |
| mmlu | ✅ | — | ✅ | ✅ |
| samsum | ✅¹ | — | — | ✅ (strip-fixed) |
| triviaqa | ✅ | ✅ | — | ✅ |
| wmt14_fren | ✅ | — | — | ✅ |
| wmt19_deen | ✅ | — | — | ✅ |
| xsum | ✅¹ | — | — | ✅ (strip-fixed) |
| truthfulqa | — | — | — | ✅ (descoped) |
## Caveats
- **Metrics**: MCQ/QA (mmlu, coqa, triviaqa) strict `Accuracy` undercounts
(verbose answers / "B. …" prefixes). Recompute with
`scripts/repatch_pickle_accuracy.py` for real numbers
(e.g. mmlu 4.8% → 33%). NMT (wmt14/wmt19) — use **Comet** (~0.80), not Accuracy.
- ¹ **samsum/xsum setup_a** carry a high meta-prefix rate (~44–66% of outputs
begin with "It seems…"), but a real summary is usually embedded after the
prefix, so **AlignScore (~0.66)** is the usable signal.
- **Missing**: coqa & gsm8k setup_a; setup_b beyond triviaqa (all other
setup_b runs were failed stubs). setup_c is derivable from setup_b/d
hidden states where present.
## Provenance
Generated on MBZUAI cluster (mbz1–4) + CIAI, model `inclusionAI/LLaDA2.1-mini`,
DAE decoding (`tau_edit=0.0`, `mbe_iters=16`, `suppress_eos_min_total_committed=50`),
`subsample_eval_dataset=2000`. See PR ArtemVazh/uncertainty_dllm#4.
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