ColossalAI/applications/ColossalEval

Table of Contents

• Overview
• Leaderboard
• Install
• Evaluation Process
  • Inference
    • Dataset Preparation
    • Configuration
    • How to Use
  • Evaluation
    • Dataset Evaluation
      • Configuration
      • How to Use
    • GPT Evaluation
      • Configuration
      • How to Use
• More Details
  • Inference Details
  • Evaluation Details
    • Metrics
  • examples
    • Dataset Evaluation Example
    • GPT Evaluation Example
• To Do
• FAQ
  • How to Add a New Metric?
  • How to Add a New Dataset?
  • How to Add a New Model?
• Citations

Overview

ColossalEval is a project which provides a uniform pipeline to help evaluate language models on different public dataset or your own dataset using both classic metrics and the help from GPTs. More details can be found in the following sections.

Leaderboard

We conducted comprehensive evaluation on 4 dataset and compare our Colossal-Llama-2-7b-base model with various models.

• We use 5-shot for MMLU and calculate scores based on the logits of first predicted token.
• We use 5-shot for CMMLU and calculate scores based on the logits of first predicted token.
• We use 5-shot for AGIEval and only calculate scores for 4-choice questions using a combination metric of exact match and the logits of first predicted token. If any of the exact match or logits of first predicted token is correct, the model will get the score.
• We use 0-shot for GAOKAO-Bench and only calculate scores for 4-choice questions based on the logits of first predicted token.
• The generation config for all dataset is greedy search.
• We also provided CEval scores from its lastest leaderboard or the official repository of the model.

More details about metrics can be found in Metrics.

	Backbone	Tokens Consumed		MMLU	CMMLU	AGIEval	GAOKAO	CEval
	-	-		5-shot	5-shot	5-shot	0-shot	5-shot
Baichuan-7B	-	1.2T		42.32 (42.30)	44.53 (44.02)	38.72	36.74	42.80
Baichuan-13B-Base	-	1.4T		50.51 (51.60)	55.73 (55.30)	47.20	51.41	53.60
Baichuan2-7B-Base	-	2.6T		46.97 (54.16)	57.67 (57.07)	45.76	52.60	54.00
Baichuan2-13B-Base	-	2.6T		54.84 (59.17)	62.62 (61.97)	52.08	58.25	58.10
ChatGLM-6B	-	1.0T		39.67 (40.63)	41.17 (-)	40.10	36.53	38.90
ChatGLM2-6B	-	1.4T		44.74 (45.46)	49.40 (-)	46.36	45.49	51.70
InternLM-7B	-	-		46.70 (51.00)	52.00 (-)	44.77	61.64	52.80
InternLM-20B	-	2.3T		60.96 (62.05)	59.08 (-)	57.96	61.92	-
Qwen-7B (original)	-	2.2T		54.29 (56.70)	56.03 (58.80)	52.47	56.42	59.60
Qwen-7B	-	2.4T		58.33 (58.20)	62.54 (62.20)	64.34	74.05	63.50
Llama-2-7B	-	2.0T		44.47 (45.30)	32.97 (-)	32.60	25.46	-
Linly-AI/Chinese-LLaMA-2-7B-hf	Llama-2-7B	1.0T		37.43	29.92	32.00	27.57	-
wenge-research/yayi-7b-llama2	Llama-2-7B	-		38.56	31.52	30.99	25.95	-
ziqingyang/chinese-llama-2-7b	Llama-2-7B	-		33.86	34.69	34.52	25.18	34.2
TigerResearch/tigerbot-7b-base	Llama-2-7B	0.3T		43.73	42.04	37.64	30.61	-
LinkSoul/Chinese-Llama-2-7b	Llama-2-7B	-		48.41	38.31	38.45	27.72	-
FlagAlpha/Atom-7B	Llama-2-7B	0.1T		49.96	41.10	39.83	33.00	-
IDEA-CCNL/Ziya-LLaMA-13B-v1.1	Llama-13B	0.11T		50.25	40.99	40.04	30.54	-
Colossal-LLaMA-2-7b-base	Llama-2-7B	0.0085T		53.06	49.89	51.48	58.82	50.20

The score in parentheses corresponds to the scores in the official repository of the model.

We use zero-shot for ChatGLM models.

To evaluate Qwen-7B on dataset MMLU, the prompt would be "xxx Answer:"(remove the space after ":") and we calculate the logits over " A", " B", " C" and " D" for Qwen-7B. Both the original and updated versions of Qwen-7B tend to be much more deterministic than other models. For example, the logits over " A" can be -inf and softmax would be exact 0.

For other models and other dataset, we calculate logits over "A", "B", "C" and "D".

Our model achieves a much better score over all other Llama-1 or Llama-2 based models and also stands out among popular open source LLMs.

Install

You should install ColossalEval in order to use it and colossal_eval is the package installed.

git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI/applications/ColossalEval
pip install .

If you want to add customized dataset or models, use pip install -e . in stead to ensure that any changes you make to the source code will immediately affect the package you install.

Evaluation Process

The evaluation process involves 2 steps which are inference and evaluation. You need to set the config for each step.

Inference

The inference process consists of two parts.

1. Preprocess and convert the original dataset.
2. Config your tokenizer and model arguments to perform zero-shot or few-shot prompting.

Dataset Preparation

In this step, the original dataset(either in csv or jsonl format) will be loaded and converted into a dict. In the conversion process, we carefully parse each subcategory and assign specific inference arguments for this subcategory.

Inference arguments are stored in a dict. The following is an example.

inference_kwargs = {
    "calculate_loss": True,
    "all_classes": ["A", "B", "C", "D"],
    "language": "Chinese",
    "pretrain": False,
    "max_new_tokens": 32
}

The inference_kwargs currently contains 5 fields:

• calculate_loss (bool, compulsory): Whether the loss on target tokens will be calculated
• all_classes (Optional[list], compulsory): Whether the subcategory is a single-choice question. Specify all available options in a list or otherwise None.
• language (str, compulsory): The language for the subcategory.
• pretrain (bool, compulsory): Whether the dataset is a pretrain dataset or not. It is usually used for calculate perplexity when you want to evaluate a model with extended context length.
• max_new_tokens (int, compulsory): The number of new tokens to generate during inference.

For example, for dataset MMLU, each subcategory consists of single-choice questions with options A, B, C and D by default and we can assign value ["A", "B", "C", "D"] to keyall_classes. For dataset C-Eval, target answers aren't provided in the test split so calculate_loss should be set as False. However, other dataset such as GAOKAO-bench contains different formats of questions and lacks some keys or metadata which can reveal what type (single-choice or multi-choice) of questions it is. Before assigning inference arguments, we first parse the dataset to decide which type of questions the subcategory belongs to and set the inference arguments accordingly.

Other than inference_kwargs, data is a list containing questions of a same subcategory. The following is a converted dataset.

{
    "dev": {
        "category 1": {"data": [], "inference_kwargs": {}},
        "category 2": {"data": [], "inference_kwargs": {}}
    },
    "test": {
        "category 1": {"data": [], "inference_kwargs": {}},
        "category 2": {"data": [], "inference_kwargs": {}}
    }
}

A data sample basically follow the format of Alpaca. It should contain the following keys:

• dataset (str, compulsory): The name of the dataset.
• split (str, compulsory): The split of the instruction.
• catrgory (str, compulsory): The category of the instruction.
• instruction (str, compulsory): The instruction for the LLM.
• input (str, optional): The additional context of the instruction.
• output (str, optional): The model output of the instruction.
• target (str, optional): The target answer for the instruction.

Example:

{
    "dev": {
        "Abstract Algebra": [
            {
                "dataset": "mmlu",
                "split": "dev",
                "category": "Abstract Algebra",
                "instruction": "The following is a single-choice question on Abstract Algebra. Answer the question by replying A, B, C or D.",
                "input": "Question: Find all c in Z_3 such that Z_3[x]/(x^2 + c) is a field.\nA. 0\nB. 1\nC. 2\nD. 3\nAnswer: ",
                "output": "",
                "target": "B"
            },
        ]
    },
    "test": {
        "Abstract Algebra": [
            {
                "dataset": "mmlu",
                "split": "test",
                "category": "Abstract Algebra",
                "instruction": "The following is a single-choice question on Abstract Algebra. Answer the question by replying A, B, C or D.",
                "input": "Question: Find the degree for the given field extension Q(sqrt(2), sqrt(3), sqrt(18)) over Q.\nA. 0\nB. 4\nC. 2\nD. 6\nAnswer: ",
                "output": "",
                "target": "B"
            },
        ]
    }
}

Configuration

In this step, you will configure your tokenizer and model arguments to infer on the given datasets.

A config file consists of two parts.

1. Model config. In model config, you need to specify model name, model path, model class, tokenizer arguments and model arguments. For model class, currently we support HuggingFaceModel, HuggingFaceCausalLM, ChatGLMModel and ChatGLMModel2. HuggingFaceModel is for models that can be loaded with AutoModel and HuggingFaceCausalLM is for models that can be loaded with AutoModelForCausalLM. ChatGLMModel and ChatGLMModel2 are for ChatGLM and ChatGLM2 models respectively. You can check all model classes in colossal_eval/models/__init__.py. If your model should set trust_remote_code as true, specify it in the tokenizer_kwargs and model_kwargs fields.
2. Dataset config. In dataset config, you need to specify dataset name, path and dataset class. Currently, we support zero-shot on dataset MMLU, CMMLU, AGIEval, GAOKAO-Bench and LongBench and few-shot on dataset MMLU, CMMLU and AGIEval. If you want to enable few shot, set few_shot as true. You can check all model classes in colossal_eval/dataset/__init__.py.

Once you have all config ready, the program will run inference on all the given datasets on all the given models.

An example config using model class HuggingFaceCausalLM and dataset class CMMLUDataset can be:

{
    "model": [
        {
            "name": "model name",
            "model_class": "HuggingFaceCausalLM",
            "parameters": {
                "path": "path to model",
                "model_max_length": 2048,
                "tokenizer_path": "path to tokenizer",
                "tokenizer_kwargs": {
                    "use_fast": false,
                    "trust_remote_code": true
                },
                "peft_path": null,
                "model_kwargs": {
                    "trust_remote_code": true
                },
                "prompt_template": "plain",
                "batch_size": 4
            }
        }
    ],
    "dataset": [
        {
            "name": "dataset name",
            "dataset_class": "CMMLUDataset",
            "debug": false,
            "few_shot": true,
            "path": "path to original dataset",
            "save_path": "path to save converted dataset"
        }
    ]
}

Currently, we support Hugging Face models. The tokenizer_kwargs is the arguments used in AutoTokenizer.from_pretrained(). The model_kwargs is the arguments used in AutoModel.from_pretrained or AutoModelForCausalLM.from_pretrained(). few_shot will be set true if you want to enable few-shot prompting for the dataset. debug will be set true if you want to verify whether your prompt is right or wrong.

How to Use

An example script can be the following. The configs/dataset_evaluation/inference.py is the same in all examples provided.

torchrun --nproc_per_node=1 inference.py \
    --config "path to config file" \
    --load_dataset \
    --inference_save_path "path to save inference results"

You should specify the path to config file in config. You can run the script without specifying load_dataset if you already save the converted dataset or otherwise set it to first load the original dataset and save the converted dataset. You should specify the path to save inference results in inference_save_path.

Evaluation

In the evaluation process, you only need to configure your evaluation parameters. You can use either public dataset or help from GPTs to do evaluation. We will introduce configuration for dataset evaluation and GPT evaluation.

Dataset Evaluation

In dataset evaluation, we calculate different metrics on the given inference results and public dataset.

Configuration

A config file for dataset evaluation consists of two parts.

1. Model config. In model config, you need to specify model name. If you want to evaluate perplexity over a pretrain dataset and calculate per-byte-perplexity, you have to add your tokenizer config and model max length.
2. Dataset config. In dataset config, you need to specify the evaluation metrics for the dataset.

Once you have all config ready, the program will run evaluation on inference results for all given models and dataset.

An example config can be:

{
    "model": [
        {
            "name": "model name"
        }
    ],
    "dataset": [
        {
            "name": "dataset name",
            "metrics": ["first_token_accuracy"]
        }
    ]
}

The above config specifies that the program will evaluate the inference results using first_token_accuracy metric.

How to Use

An example script can be the following.

python eval_dataset.py \
    --config "path to config file" \
    --inference_results_path "path to inference results" \
    --evaluation_results_save_path "path to save evaluation results"

You should specify the path to config file in config, the path to inference results in inference_results_path and the path to save evaluation results in evaluation_save_path.

GPT Evaluation

In GPT evaluation, we provide a prompt template which can fit in different pre-defined metrics with Chain-of-Thoughts. In the following sections, we will only introduce how you can evaluate model answers using GPTs. More details can be found in colossal_eval/evaluate/GPT Evaluation.md.

Configuration

The following is an example of a English config file. The configuration file can control how the pipeline evaluates the model. You need to specify GPT evaluation metrics. You can find an example English config file in configs/gpt_evaluation.

{
    "language": "en",
    "category": {
        "brainstorming": {
            "GPT": [
                "language organization",
                "relevance",
                "creativity",
                "practicality",
                "reasonableness"
            ]
        },
    }
}

How to Use

After setting the config file, you can evaluate the model using examples/gpt_evaluation/eval.py. If you want to make comparisons between answers of two different models, you should specify two answer files in the argument answer_file_list and two model names in the argument model_name_list(details can be found in colossal_eval/evaluate/GPT Evaluation.md). If you want to evaluate one answer file, the length of both answer_file_list and model_name_list should be 1 and the program will perform evaluation using GPTs. The prompt files for battle and gpt evaluation can be found in configs/gpt_evaluation/prompt. target file is the path to the converted dataset you save during inference time.

An example script is provided as follows:

python eval.py \
    --config_file "path to the config file" \
    --battle_prompt_file "path to the prompt file for battle" \
    --gpt_evaluation_prompt_file "path to the prompt file for gpt evaluation" \
    --target_file "path to the target answer file" \
    --answer_file_list "path to the answer file" \
    --model_name_list "the names of the model" \
    --gpt_model "which GPT model to use for evaluation" \
    --save_path "path to save results" \
    --openai_key "your openai key" \

More Details

Inference

In the inference process, we will do generation, calculate loss over target tokens, calculate number of target tokens, softmax over given options (for example, "A", "B", "C", and "D") according to the inference arguments.

For tokenization, we adopt tokenization strategy in LongBench to preserve crucial instructions on the left and right side and keep all target tokens.

For labeling target tokens, we adopt method from FastChat, but it doesn't always hold true due to tokenizers' different behavior. We plan to insert special tokens to correctly label the target tokens.

For calculating loss, we return per-sample-loss instead of per-batch-loss if we directly use model(batch).loss provided in HuggingFace.

Evaluation

To make it more easier to set the config, you only need to specify all metrics you want to use in key metrics. However, the program will only use a subset of metrics you give for different subcategories. Applying all metrics to all subcategories is obviously unsuitable. The suggested metrics for specific categories should be defined in colossal_eval/evaluate/dataset_evaluator/metrics.py.

Metrics

• combined_single_choice_accuracy: A combination of first_token_logit and single_choice_accuracy. If one of these is correct, the model will get the score. It can be used in all dataset that contains single-choice questions.
• first_token_logit: Calculate score based on softmax score over the given choices. If the argmax of the softmax is equal to the reference, the model will get the score. If there is NaN in softmax score, it will calculate the score using exact match. It can be used in all dataset that contains single-choice questions.
• single_choice_accuracy: Calculate score using exact match. It will only get the first uppercase letter such as A, B, C or D that is not surrouded by lowercase letters. If the uppercase letter is equal to the reference, the model will get the score. It can be used in all dataset that contains single-choice questions.
• multi_choice_accuracy: Calculate score on multi-choice questions. It will get a set of all uppercase letters such as A, B, C or D that is not surrouded by lowercase letters. If the prediction conatains uppercase letters that are not in reference. The model will get 0 score. If the prediction contains a uppercase letter that is in reference, the model will get a score of 1/len(reference). It is used in AGIEval and GAOKAO-Bench.
• math_equivalence: Code from hendrycks. Compute scores over the prediction math formula and reference math formula. It is used in AGIEval and GAOKAO-Bench.
• f1_score: Calculate English f1 score between prediction and reference. It is used in Longbench.
• f1_zh_score: Calculate Chinese f1 score between prediction and reference. It is used in Longbench.
• rouge_score: Calculate English f1 score between prediction and reference. It is used in GAOKAO-Bench and LongBench.
• rouge_zh_score: Calculate Chinese rouge score between prediction and reference. It is used in GAOKAO-Bench and LongBench.
• retrieval_score: Calculate English retrieval score between prediction and reference. It determines whether the ouput(which paragraph) corresponds to the given abstract. It is used in Longbench.
• retrieval_zh_score: Calculate Chinese retrieval score between prediction and reference. It determines whether the ouput(which paragraph) corresponds to the given abstract. It is used in Longbench.
• classification_score: Calculate classification score between prediction and reference. It determines whether the ouput(a class) is equal to the reference. It is used in Longbench.
• code_sim_score: Calculate similarity score between prediction and reference. It is used in Longbench.
• count_score: Calculate count score between prediction and reference. It determines whether the ouput(number of given passages) is equal to the reference. It is used in Longbench.
• perplexity: Calculate perplexity. The formula is perplexity = \frac{1}{n} \sum_i e^{loss_i} where n is the number of samples and loss_i is the average loss for sample i. It can be used in all dataset.
• ppl_score: Calculate perplexity score. The formula is ppl\_score = \frac{1}{n} \sum_i e^{-loss_i} where n is the number of samples and loss_i is the average loss for sample i. It can be used in all dataset.
• ppl_score_over_choices: Calculate perplexity score over choices. The formula is ppl\_score\_over\_choices= \frac{1}{n} \sum_i e^{-loss\_over\_choices_i} where n is the number of samples and loss\_over\_choices_i is the loss on the first predicted token for sample i. It can be used in all dataset that contains single-choice questions.
• per_byte_perplexity: Calculate per byte perplexity. The formula is \frac{1}{n} \sum_i e^{\frac{loss_i}{byte_i}} where n is the number of samples, loss_i is the total loss for sample i and byte_i is the number of bytes sample i occupies. It can be used in all dataset.
• per_byte_ppl_score: Calculate per byte perplexity score. The formula is \frac{1}{n} \sum_i e^{-\frac{loss_i}{byte_i}} where n is the number of samples, loss_i is the total loss for sample i and byte_i is the number of bytes sample i occupies. It can be used in all dataset.

We use combined_single_choice_accuracy and first_token_logit in the leaderboard.

Examples

We provide 2 examples for you to explore our colossal_eval package.

Dataset Evaluation Example

This example is in folder examples/dataset_evaluation.

1. cd examples/dataset_evaluation
2. Fill in your inference config file in config/inference/config.json. Set the model and dataset parameters.
3. Run inference.sh to get inference results.
4. Fill in your evaluation config file in config/evaluation/config.json. Set the model and dataset parameters.
5. Run eval_dataset.sh to get evaluation results.

GPT Evaluation Example

The examples is in folder examples/gpt_evaluation.

1. cd examples/gpt_evaluation
2. Fill in your inference config file in config/inference/config.json. Set the model and dataset parameters. If you want to use the example dataset we provide, the dataset is ColossalDataset.
3. Run inference.sh to get inference results.
4. Fill in your evaluation config file in config/evaluation/config.json.
5. Run eval.sh to get evaluation results.

FAQ

How to Add a New Metric?

If you want to add a customized metric, we recommend using pip install -e . to ensure that any changes you make to the source code will immediately affect the package you install.

To add a new metric, you can follow the example of multi_choice_accuracy in line 339 in colossal_eval/evaluate/dataset_evaluator/metric.py. The method take one data sample's prediction and reference as input and return a score ranging from 0 to 1.

A skeleton of code is the following.

def CustomizedMetric(prediction: str, reference: str):
	score = xxx
	return score

Once you have successfully added your own metric, you should specify your metric both in colossal_eval/evaluate/dataset_evaluator/metric.py (suggest which subcategories shoule the metric be applied to) and your evaluation config.

How to Add a New Dataset?

If you want to add customized dataset, we recommend using pip install -e . to ensure that any changes you make to the source code will immediately affect the package you install.

To add a new dataset, you can follow the example of colossal_eval/dataset/mmlu.py. You need to make sure that the format of questions in one subcategory should be the same. For example, all questions should have target answers or all questions should be single-choice questions.

A skeleton of code is the following.

class CustomizedDataset(BaseDataset):
    @staticmethod
    def load():
        # 1. Load and convert the original dataset format.
    	# 2. Assign inference arguments for each subcategory.
    	# 3. Return the converted dataset.
    	pass

Once you have successfully added your own dataset, you can specify your dataset class in your inference config.

How to Add a New Model?

If you want to add customized models, we recommend using pip install -e . to ensure that any changes you make to the source code will immediately affect the package you install.

To add a new model, you can follow the example of colossal_eval/models/huggingface.py. You need to provide a way to load the model and tokenizer, calculate loss and generate.

A skeleton of code is the following.

class CustomizedModel(BaseModel):
    def __init__(self):
        super().__init__()
		self._load_tokenizer()
		self._load_model()

	def _load_tokenizer():
		pass

	def _load_model():
		pass

	def _calculate_loss():
		pass

	def get_loss():
		self._calculate_loss()

	def inference(samples):
		# 1. Load samples from the same subcategory.
		# 2. Infer in a batch way according to inference arguments.
		# 3. Return results.
		batch_samples = xxx
		self.get_loss(batch_samples)
		self.generate(batch_samples)

		return inference_results

	def generate():
		pass

Once you have successfully added your own model, you can specify your model class in your inference config.

To do

• Add visualization code for evaluation results on public dataset
• Improve the way to label target tokens

Citations

@misc{zhong2023agieval,
      title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
      author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
      year={2023},
      eprint={2304.06364},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{huang2023ceval,
title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models},
author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian},
journal={arXiv preprint arXiv:2305.08322},
year={2023}
}

@misc{li2023cmmlu,
      title={CMMLU: Measuring massive multitask language understanding in Chinese},
      author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
      year={2023},
      eprint={2306.09212},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{Zhang2023EvaluatingTP,
  title={Evaluating the Performance of Large Language Models on GAOKAO Benchmark},
  author={Xiaotian Zhang and Chunyang Li and Yi Zong and Zhengyu Ying and Liang He and Xipeng Qiu},
  year={2023}
}

@misc{bai2023longbench,
      title={LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding},
      author={Yushi Bai and Xin Lv and Jiajie Zhang and Hongchang Lyu and Jiankai Tang and Zhidian Huang and Zhengxiao Du and Xiao Liu and Aohan Zeng and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li},
      year={2023},
      eprint={2308.14508},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{hendryckstest2021,
  title={Measuring Massive Multitask Language Understanding},
  author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2021}
}

@article{hendrycks2021ethics,
  title={Aligning AI With Shared Human Values},
  author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2021}
}

@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}
}