DIAL: an LLM-judge panel in both display orders

Data released with DIAL: Position-Debiased LLM Judges with Adaptive Human Preference Calibration (Cai, Fan, Chen, and Du, 2026). Code: github.com/JinHongDu-Lab/DIAL.

Every human-labelled pairwise comparison in three preference benchmarks was judged anonymously by a panel of 22 LLM judges, once in the original display order and once with the two responses swapped. The result is 429,879 judge responses over 9,882 comparisons, with the human label attached to every row, for studying position bias, judge heterogeneity, and human alignment of LLM-as-a-judge.

Config	Rows	Contents
arena_33k_benchmark	7,738	Chatbot Arena comparisons (20 models) with human labels
arena_33k_responses	337,105	judge responses on those comparisons
mt_bench_benchmark	1,199	MT-Bench turn-1 comparisons (6 models) with human labels
mt_bench_responses	51,561	judge responses on those comparisons
pandalm_benchmark	945	PandaLM test comparisons (5 models) with human labels
pandalm_responses	41,213	judge responses on those comparisons

Loading

from datasets import load_dataset

responses = load_dataset("CSML/DIAL", "mt_bench_responses", split="train")
benchmark = load_dataset("CSML/DIAL", "mt_bench_benchmark", split="train")

Join responses["record_id"] to benchmark["id"] to recover the prompt and the two responses a judge saw. Every file is plain Parquet, so pandas.read_parquet on the raw files works as well.

Benchmarks

Config prefix	Source	Human label
arena_33k	Chatbot Arena conversations (33K), restricted to the records covered by the Judge-Aware Ranking framework's judge-vs-human comparisons (Xu et al., 2026)	the Arena vote
mt_bench	MT-Bench human judgments, turn 1	majority vote over annotators per (question, model pair)
pandalm	PandaLM human-annotated test set	majority vote over 3 annotators

Each benchmark row has id, model_a, model_b, winner (model_a, model_b, tie, or, for Arena, both_bad), prompt, response_a, and response_b, plus source-specific columns such as language (Arena; 89% English), question_id, and num_human_annotators. The Arena file also carries the Judge-Aware Ranking release's own single-judge verdict for each record (ja_judge_model, ja_judge_preferred_model_original_order, ja_judge_confidence), redistributed from their data; the DIAL paper does not use these columns.

Judge responses

The panel has 22 judges: 19 open-weight models served locally by Ollama and three paid batch APIs (openai-gpt-4.1-nano-batch-direct, anthropic-claude-haiku-4.5-batch-direct, google-gemini-3.1-flash-lite-batch-minimal). configs/judges.json records each model tag, quantization, and decoding setting; <dataset>/study.toml records the study design and <dataset>/prompts/ the system and user prompts. Every record is judged in both display orders (original_anonymous and swapped_anonymous), always with model names hidden, and the judge answers with one line <choice> <confidence>.

Column	Meaning
record_id	benchmark row (id in the benchmark config)
panel_judge	judge alias in the study panel; the identity to group by
judge	alias of the exact query (a few rows carry a -64tok re-query suffix)
judge_model, judge_provider, inference	model tag, backend, and decoding settings (JSON)
condition_id	original_anonymous or swapped_anonymous
response_order_default	true when model_a was displayed first
choice	displayed slot chosen: a (first), b (second), c (tie)
canonical_verdict	the same verdict mapped back to model_a / model_b / tie
confidence	the judge's self-reported confidence, 1 to 5
raw_response	the judge's raw output
human_winner	human label of the record
prompt_hash, *_fingerprint	hashes of the exact prompt and configuration used
usage, latency_seconds, completed_at, provider_*	collection metadata

Position-bias quantities read choice; agreement with human_winner reads canonical_verdict. The paper excludes ollama-stablelm2-12b-direct (tie rate above 50% on every benchmark), leaving 21 judges, and drops LLM ties and human ties or both_bad labels, which gives the 410K judgments reported there; the release keeps everything.

Collection

Responses were collected between August and September 2026 with the kit in data/judge_query of the code repository. Open-weight judges ran through a local Ollama server at the tags listed in configs/judges.json; the tag and quantization are part of the judge's identity. Paid judges were queried through the providers' batch APIs at temperature 0 where the API allows it.

Licensing

The judge responses, prompts, and study configuration are released under CC BY-NC 4.0. The benchmark files redistribute third-party material under their own terms: Chatbot Arena user prompts are CC BY 4.0 and model outputs are CC BY-NC 4.0 (see the source dataset's terms), the MT-Bench human judgments are CC BY 4.0, and PandaLM is Apache 2.0. The non-commercial term on the Arena model outputs is what sets the license of this dataset as a whole.

Citation

@article{cai2026dial,
  title   = {{DIAL}: Position-Debiased {LLM} Judges with Adaptive Human Preference Calibration},
  author  = {Cai, Zesheng and Fan, Yingqi and Chen, Sichang and Du, Jin-Hong},
  journal = {arXiv preprint arXiv:2609.31215},
  year    = {2026}
}

Please also cite the source benchmarks and, for the Arena subset, the Judge-Aware Ranking framework:

@inproceedings{zheng2023mtbench,
  title     = {Judging {LLM}-as-a-Judge with {MT}-Bench and Chatbot Arena},
  author    = {Zheng, Lianmin and Chiang, Wei-Lin and Sheng, Ying and Zhuang, Siyuan and Wu, Zhanghao and Zhuang, Yonghao and Lin, Zi and Li, Zhuohan and Li, Dacheng and Xing, Eric P. and Zhang, Hao and Gonzalez, Joseph E. and Stoica, Ion},
  booktitle = {Advances in Neural Information Processing Systems},
  volume    = {36},
  pages     = {46595--46623},
  year      = {2023}
}
@inproceedings{chiang2024chatbot,
  title     = {Chatbot Arena: An Open Platform for Evaluating {LLM}s by Human Preference},
  author    = {Chiang, Wei-Lin and Zheng, Lianmin and Sheng, Ying and Angelopoulos, Anastasios N. and Li, Tianle and Li, Dacheng and Zhu, Banghua and Zhang, Hao and Jordan, Michael I. and Gonzalez, Joseph E. and Stoica, Ion},
  booktitle = {International Conference on Machine Learning},
  pages     = {8359--8388},
  year      = {2024}
}
@inproceedings{wang2024pandalm,
  title     = {{PandaLM}: An Automatic Evaluation Benchmark for {LLM} Instruction Tuning Optimization},
  author    = {Wang, Yidong and Yu, Zhuohao and Yao, Wenjin and Zeng, Zhengran and Yang, Linyi and Wang, Cunxiang and Chen, Hao and Jiang, Chaoya and Xie, Rui and Wang, Jindong and Xie, Xing and Ye, Wei and Zhang, Shikun and Zhang, Yue},
  booktitle = {The Twelfth International Conference on Learning Representations},
  year      = {2024}
}
@inproceedings{xu2026judgeaware,
  title     = {A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth},
  author    = {Xu, Mingyuan and Tan, Xinzi and Wu, Jiawei and Zhou, Doudou},
  booktitle = {Forty-third International Conference on Machine Learning},
  year      = {2026}
}