
## Content
This dataset contains 3.3K expert-level pairwise human preferences for model responses generated by 6 models in response to 80 MT-bench questions.
The 6 models are GPT-4, GPT-3.5, Claud-v1, Vicuna-13B, Alpaca-13B, and LLaMA-13B. The annotators are mostly graduate students with expertise in the topic areas of each of the questions. The details of data collection can be found in our [paper](https://arxiv.org/abs/2306.05685).

## Agreement Calculation
This Colab [notebook](https://colab.research.google.com/drive/1ctgygDRJhVGUJTQy8-bRZCl1WNcT8De6?usp=sharing) shows how to compute the agreement between humans and GPT-4 judge with the dataset. Our results show that humans and GPT-4 judge achieve over 80\% agreement, the same level of agreement between humans.

## Citation
```
@misc{zheng2023judging,
      title={Judging LLM-as-a-judge with MT-Bench and Chatbot Arena}, 
      author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zi Lin and Zhuohan Li and Dacheng Li and Eric. P Xing and Hao Zhang and Joseph E. Gonzalez and Ion Stoica},
      year={2023},
      eprint={2306.05685},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

task_categories:conversational task_categories:question-answering size_categories:1K<n<10K language:en license:cc-by-4.0 arxiv:2306.05685 region:us
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["conversational", "question-answering"], "dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "model_a", "dtype": "string"}, {"name": "model_b", "dtype": "string"}, {"name": "winner", "dtype": "string"}, {"name": "judge", "dtype": "string"}, {"name": "conversation_a", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "conversation_b", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "turn", "dtype": "int64"}], "splits": [{"name": "human", "num_bytes": 15003469, "num_examples": 3355}, {"name": "gpt4_pair", "num_bytes": 10679650, "num_examples": 2400}], "download_size": 1388888, "dataset_size": 25683119}}
TAGS
#task_categories-conversational #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #arxiv-2306.05685 #region-us 


## Content
This dataset contains 3.3K expert-level pairwise human preferences for model responses generated by 6 models in response to 80 MT-bench questions.
The 6 models are GPT-4, GPT-3.5, Claud-v1, Vicuna-13B, Alpaca-13B, and LLaMA-13B. The annotators are mostly graduate students with expertise in the topic areas of each of the questions. The details of data collection can be found in our paper.

## Agreement Calculation
This Colab notebook shows how to compute the agreement between humans and GPT-4 judge with the dataset. Our results show that humans and GPT-4 judge achieve over 80\% agreement, the same level of agreement between humans.


## Content
This dataset contains 3.3K expert-level pairwise human preferences for model responses generated by 6 models in response to 80 MT-bench questions.
The 6 models are GPT-4, GPT-3.5, Claud-v1, Vicuna-13B, Alpaca-13B, and LLaMA-13B. The annotators are mostly graduate students with expertise in the topic areas of each of the questions. The details of data collection can be found in our paper. ## Agreement Calculation
This Colab notebook shows how to compute the agreement between humans and GPT-4 judge with the dataset. Our results show that humans and GPT-4 judge achieve over 80\% agreement, the same level of agreement between humans.
TAGS
#task_categories-conversational #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #arxiv-2306.05685 #region-us 
 ## Content
This dataset contains 3.3K expert-level pairwise human preferences for model responses generated by 6 models in response to 80 MT-bench questions.
The 6 models are GPT-4, GPT-3.5, Claud-v1, Vicuna-13B, Alpaca-13B, and LLaMA-13B. The annotators are mostly graduate students with expertise in the topic areas of each of the questions. The details of data collection can be found in our paper. ## Agreement Calculation
This Colab notebook shows how to compute the agreement between humans and GPT-4 judge with the dataset. Our results show that humans and GPT-4 judge achieve over 80\% agreement, the same level of agreement between humans.
passage: TAGS
#task_categories-conversational #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #arxiv-2306.05685 #region-us 
## Content
This dataset contains 3.3K expert-level pairwise human preferences for model responses generated by 6 models in response to 80 MT-bench questions.
The 6 models are GPT-4, GPT-3.5, Claud-v1, Vicuna-13B, Alpaca-13B, and LLaMA-13B. The annotators are mostly graduate students with expertise in the topic areas of each of the questions. The details of data collection can be found in our paper.## Agreement Calculation
This Colab notebook shows how to compute the agreement between humans and GPT-4 judge with the dataset. Our results show that humans and GPT-4 judge achieve over 80\% agreement, the same level of agreement between humans.