---
license: other
license_name: softelo-and-upstream-data-terms
license_link: https://huggingface.co/datasets/kbora/SoftElo/blob/main/DATA_LICENSES.md
pretty_name: Soft-Elo Judge Annotations
size_categories:
  - 100K<n<1M
tags:
  - llm-as-a-judge
  - human-preferences
  - bradley-terry
  - calibration
  - conformal-prediction
configs:
  - config_name: lmarena100k
    data_files:
      - split: annotations
        path: data/lmarena100k/*.parquet
  - config_name: lmarena140k
    data_files:
      - split: annotations
        path: data/lmarena140k/*.parquet
---

# Soft-Elo Judge Annotations

Judge scores and preferences for **From Uncertain Judgments to Calibrated
Rankings: Conformal Elo Estimation for LLM Evaluation**, Bora Kargi and David
Salinas, NeurIPS 2026.

**Code repository:** [kargibora/SoftElo](https://github.com/kargibora/SoftElo).
**Dataset:** [kbora/SoftElo](https://huggingface.co/datasets/kbora/SoftElo).

The release contains 368,777 judge–battle annotation rows from two public
human-preference corpora. It supports alternative judge calibration, rating
aggregation, committee and position-bias analyses without rerunning the judges.
Each row corresponds to one judge's annotation of one battle. Counts below
include invalid or incomplete scores; validity flags make the paper's filtering
explicit.

| Corpus | Judges | Annotation rows | Battles valid for every released judge |
| --- | ---: | ---: | ---: |
| lmarena100k | 8 | 199,922 | 18,158 |
| lmarena140k | 7 | 168,855 | 18,927 |

Annotation rows count repeated judgments of the same battle by different judges.
The last column counts only the intersection across every released judge; it is
not the number of battles available to each judge. All per-judge rows are
released.

The `annotations` split is a storage name, not a training/test partition.
Evaluation holds out models. Do not randomly split annotation rows and describe
that as the paper's model-holdout evaluation.

## Sources and attribution

- [LMArena 100K](https://huggingface.co/datasets/lmarena-ai/arena-human-preference-100k):
  human preferences, model identities and source battle IDs.
- [LMArena 140K](https://huggingface.co/datasets/lmarena-ai/arena-human-preference-140k):
  later Arena preferences and model identities.

The added annotations are LLM-generated scores and preferences, not additional
human votes. Cite the upstream datasets and the Soft-Elo paper when using them.
Source terms are described in [DATA_LICENSES.md](DATA_LICENSES.md).

## Loading

Load either LMArena configuration directly from the Hub. The dataset is public;
no authentication is required.

```python
from datasets import load_dataset

data = load_dataset(
    "kbora/SoftElo", name="lmarena100k", split="annotations", token=True
)
```

Use `name="lmarena140k"` for the second corpus. For reproducible analyses, pass
`revision=` with the full dataset commit hash.

### Use with the GitHub repository

The [SoftElo repository](https://github.com/kargibora/SoftElo) accepts one judge's
annotations as a CSV. From the loaded dataset, preserve the original row order
and select the paper-valid rows:

```python
frame = data.to_pandas()
frame = frame.loc[
    (frame["judge"] == "GPT-OSS-120B") & frame["paper_valid"]
].sort_values("row_index")
frame.to_csv("softelo_gpt_oss_120b.csv", index=False)
```

From a checkout of that repository:

```bash
python run_demo.py --data /path/to/softelo_gpt_oss_120b.csv --out outputs_gpt120
```

The CSV retains the score dictionaries as JSON strings, as expected by the
GitHub loader. This example connects the data formats; the bundled reproduction
package below documents the protocols for matching the archived paper tables.

The [Python package](code/README.md) loads a selected judge or paper subset,
parses score dictionaries and applies the exact documented preference processing.
It also provides commands to rebuild archived tables or recompute ratings.

## Fields and orientation

| Field | Meaning |
| --- | --- |
| `battle_id` | SHA-256 of corpus, source ID and sorted model pair; shared across judges and A/B orientation |
| `source_id`, `source_revision` | Join to the pinned upstream source record |
| `source_dataset`, `judge` | Corpus and paper judge identifier |
| `instruction_id`, `row_index` | Original experiment identity and row order |
| `model_a`, `model_b` | Model identities in this annotation's orientation |
| `source_order_reversed` | Whether annotation A/B is reversed relative to the linked source record |
| `human_pref`, `judge_pref` | Raw preferences: 0 = A wins, 1 = B wins, 0.5 = tie; weak values 0.25/0.75 are retained |
| `scores_a`, `scores_b` | Aggregated six-criterion scores, encoded as JSON objects |
| `scores_a_original`, `scores_b_original` | Scores with A presented first, where retained |
| `scores_a_swapped`, `scores_b_swapped` | Scores with B presented first, mapped to the same model A/B identities |
| `preference_original`, `preference_swapped` | Model-oriented preferences for each presentation order, where retained |
| `has_order_scores`, `has_order_preferences` | Availability of those per-order fields |
| `paper_valid` | Original per-judge score-validity rule: dictionaries with no missing/nonfinite score values |
| `shared_eligible` | Stricter shared-support rule: finite scores in [1,10], valid preference labels and no parse error |
| `language`, `len_a`, `len_b`, `parse_error` | Available annotation metadata |

Criteria are adherence, clarity, completeness, factuality, fluency and helpfulness.
JSON objects preserve missing score values as null. Unavailable per-order objects
are null, not fabricated duplicates of the aggregate. Five older LMArena100K
judge files retain only aggregated scores; see per-judge availability counts in
[manifest.json](manifest.json). Metadata files contain the saved system prompt,
rubric and available judge settings; unknown settings remain null.

## Paper subsets

`paper_valid` is the filter used for the per-judge main rating analysis. The
reference loader subsequently maps preferences within 0.26 of 0.5 to ties.

`subsets/*_shared_valid.parquet` lists the intersection of eligible source
records and model pairs across all released judges in each corpus. Human
preferences agree after aligning A/B orientation. The LMArena100K intersection
contains exactly the 18,158 battles used by the paper's committee and human
reference analyses. The LMArena140K intersection is supplied for reuse; it
is not a claim that the paper evaluated all judges on that intersection.

Preserve all per-judge rows for reproducing the main results. Selecting only the
shared intersection changes the experiment.

## Source versions

Every exported source ID, model pair and human label was checked against a
pinned upstream snapshot. Use each row's `source_revision` when joining.

The original annotation files did not record upstream commit IDs. These are
verified compatible snapshots, not a reconstruction of unrecorded run metadata.
Battle identifiers match records across versions by source ID and model pair;
exact historical conversation-text equality across versions has not been
independently checked. Original text can be fetched from the pinned upstream
sources with `load_source_rows` in the Python package.

## Reproduction and limits

The package provides explicit protocols for the main fold-specific-temperature
analysis, pooled-temperature ablations, constant smoothing and the historical
conformal calculation. Archived main model-level predictions and conformal split
measurements are included under `results/`. The smoothing archive contains
per-judge MAEs; its original model-level predictions were not saved.

Fresh CPU fits reproduce all 31 judge/protocol combinations: eight main,
eight smoothing, eight historical conformal and seven LMArena140K comparisons.
See [code/VERIFICATION.md](code/VERIFICATION.md) for the reference scope and
machine-readable checks. The audit corrected Hard-Elo anchor bootstrapping in
the release code; the saved reference results are unchanged. This release does
not include auxiliary prompt-variation experiments or the author re-annotation study.

The scores reflect judge errors and biases. Human votes are observed preferences,
not noise-free ground truth. Calibration and conformal coverage may change under
model-population or prompt-distribution shifts. Missing per-order fields limit
position-bias analyses for those judges. This is an annotation dataset; it does
not include raw user prompts, candidate responses, user identifiers or judge
explanations.

## Citation

```bibtex
@inproceedings{kargi2026softelo,
  title = {From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation},
  author = {Kargi, Bora and Salinas, David},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2026}
}
```

For Arena, also cite Chiang et al., *Chatbot Arena: An Open Platform for
Evaluating LLMs by Human Preference* (2024), and Tang, Chiang and Angelopoulos,
*Arena Explorer: A Topic Modeling Pipeline for LLM Evals & Analytics* (2025),
as requested by the source dataset card.