| --- |
| 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. |
|
|
