---
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.