ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems

Table of Contents: Installation | Requirements | Quick Start | Citation

ARES is a groundbreaking framework for evaluating Retrieval-Augmented Generation (RAG) models. The automated process combines synthetic data generation with fine-tuned classifiers to efficiently assess context relevance, answer faithfulness, and answer relevance, minimizing the need for extensive human annotations. ARES employs synthetic query generation and Prediction-Powered Inference (PPI), providing accurate evaluations with statistical confidence.

💬 Mini Q&A

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What does ARES assess in RAG models?

ARES conducts a comprehensive evaluation of Retrieval-Augmented Generation (RAG) models, assessing the systems for context relevance, answer faithfulness, and answer relevance. This thorough assessment ensures a complete understanding of the performance of the RAG system.

How does ARES automate the evaluation process?

ARES minimizes the need for human labeling by leveraging fine-tuned classifiers and synthetic data. Its PPI component, Prediction-Powered inference, refines evaluations considering model response variability and provides statistical confidence in the results. By using fine-tuned classifiers and synthetically generated data, ARES cuts down on human labeling needs while providing accurate assessments.

Can ARES handle my custom RAG model?

Yes, ARES is a model-agnostic tool that enables you to generate synthetic queries and answers from your documents. With ARES, you can evaluate these generated queries and answers from your RAG model. ​

⚙️ Installation

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​ To install ARES, run the following commands: ​

pip install ares-ai

​ Optional: Initalize OpenAI or TogetherAI API key with the following command:

export OPENAI_API_KEY=<your key here>
export TOGETHER_API_KEY=<your key here>

📝 Requirements

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To implement ARES for scoring your RAG system and comparing to other RAG configurations, you need three components:​

• A human preference validation set of annotated query, document, and answer triples for the evaluation criteria (e.g. context relevance, answer faithfulness, and/or answer relevance). There should be at least 50 examples but several hundred examples is ideal.
• A set of few-shot examples for scoring context relevance, answer faithfulness, and/or answer relevance in your system
• A much larger set of unlabeled query-document-answer triples outputted by your RAG system for scoring

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To get started with ARES, you'll need to set up your configuration. Below is an example of a configuration for ARES!

Copy-paste each step to see ARES in action!

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📥 Download datasets

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Use the following command to quickly obtain the necessary files for getting started! This includes the 'few_shot_prompt' file for judge scoring and synthetic query generation, as well as both labeled and unlabeled datasets.

wget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_few_shot_prompt_for_judge_scoring.tsv
wget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_few_shot_prompt_for_synthetic_query_generation.tsv
wget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_labeled_output.tsv
wget https://raw.githubusercontent.com/stanford-futuredata/ARES/main/datasets/example_files/nq_unlabeled_output.tsv

OPTIONAL: You can run the following command to get the full NQ dataset! (37.3 GB)

from ares import ARES
ares = ARES() 
ares.KILT_dataset("nq")

# Fetches NQ datasets with ratios including 0.5, 0.6, 0.7, etc.
# For purposes of our quick start guide, we rename nq_ratio_0.5 to nq_unlabeled_output and nq_labeled_output.

---

🚀 Quick Start - #1

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To get started with ARES's PPI, you'll need to set up your configuration. Below is an example of a configuration for ARES!

Just copy-paste as you go to see ARES in action!

Step 1) Run the following to retrieve the UES/IDP scores with GPT3.5!

from ares import ARES

ues_idp_config = {
    "in_domain_prompts_dataset": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "unlabeled_evaluation_set": "nq_unlabeled_output.tsv", 
    "model_choice" : "gpt-3.5-turbo-0125"
} 

ares = ARES(ues_idp=ues_idp_config)
results = ares.ues_idp()
print(results)
# {'Context Relevance Scores': [Score], 'Answer Faithfulness Scores': [Score], 'Answer Relevance Scores': [Score]}

Step 2) Run the following to retrive ARES's PPI scores with GPT3.5!

ppi_config = { 
    "evaluation_datasets": ['nq_unlabeled_output.tsv'], 
    "few_shot_examples_filepath": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "llm_judge": "gpt-3.5-turbo-1106",
    "labels": ["Context_Relevance_Label"], 
    "gold_label_path": "nq_labeled_output.tsv", 
}

ares = ARES(ppi=ppi_config)
results = ares.evaluate_RAG()
print(results)

---

🚀 Quick Start - #2

---

Step 1) Run the following to see GPT 3.5's accuracy on the NQ unlabeled dataset!

from ares import ARES

ues_idp_config = {
    "in_domain_prompts_dataset": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "unlabeled_evaluation_set": "nq_unlabeled_output.tsv", 
    "model_choice" : "gpt-3.5-turbo-0125"
} 

ares = ARES(ues_idp=ues_idp_config)
results = ares.ues_idp()
print(results)
# {'Context Relevance Scores': [Score], 'Answer Faithfulness Scores': [Score], 'Answer Relevance Scores': [Score]}

Step 2) Run the following to see ARES's synthetic generation in action!

from ares import ARES

synth_config = { 
    "document_filepaths": ["nq_labeled_output.tsv"] ,
    "few_shot_prompt_filename": "nq_few_shot_prompt_for_synthetic_query_generation.tsv",
    "synthetic_queries_filenames": ["synthetic_queries_1.tsv"], 
    "documents_sampled": 6189
}

ares_module = ARES(synthetic_query_generator=synth_config)
results = ares_module.generate_synthetic_data()
print(results)

---

Step 3) Run the following to see ARES's training classifier in action!

from ares import ARES

classifier_config = {
    "training_dataset": ["synthetic_queries_1.tsv"], 
    "validation_set": ["nq_labeled_output.tsv"], 
    "label_column": ["Context_Relevance_Label"], 
    "num_epochs": 10, 
    "patience_value": 3, 
    "learning_rate": 5e-6,
    "assigned_batch_size": 1,  
    "gradient_accumulation_multiplier": 32,  
}

ares = ARES(classifier_model=classifier_config)
results = ares.train_classifier()
print(results)

Note: This code creates a checkpoint for the trained classifier. Training may take some time. You can download our jointly trained checkpoint on context relevance here!: Download Checkpoint

---

Step 4) Run the following to see ARES's PPI in action!

from ares import ARES

ppi_config = { 
    "evaluation_datasets": ['nq_unlabeled_output.tsv'], 
    "checkpoints": ["Context_Relevance_Label_nq_labeled_output_date_time.pt"], 
    "rag_type": "question_answering", 
    "labels": ["Context_Relevance_Label"], 
    "gold_label_path": "nq_labeled_output.tsv", 
}

ares = ARES(ppi=ppi_config)
results = ares.evaluate_RAG()
print(results)

# Output Should be: 
""" 
Context_Relevance_Label Scoring
ARES Ranking
ARES Prediction: [0.6056978059262574]
ARES Confidence Interval: [[0.547, 0.664]]
Number of Examples in Evaluation Set: [4421]
Ground Truth Performance: [0.6]
ARES LLM Judge Accuracy on Ground Truth Labels: [0.789]
Annotated Examples used for PPI: 300
"""

🚀 Local Model Execution with vLLM

ARES supports vLLM, allowing for local execution of LLM models, offering enhanced privacy and the ability to operate ARES offline. Below are steps to vLLM for ARES's UES/IDP and PPI!

1) UES/IDP w/ vLLM

from ares import ARES

ues_idp_config = {
    "in_domain_prompts_dataset": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "unlabeled_evaluation_set": "nq_unlabeled_output.tsv", 
    "model_choice": "meta-llama/Llama-2-13b-hf", # Specify vLLM model
    "vllm": True, # Toggle vLLM to True 
    "host_url": "http://0.0.0.0:8000/v1" # Replace with server hosting model followed by "/v1"
} 

ares = ARES(ues_idp=ues_idp_config)
results = ares.ues_idp()
print(results)

---

2) PPI w/ vLLM

from ares import ARES

ppi_config = { 
    "evaluation_datasets": ['nq_unabeled_output.tsv'], 
    "few_shot_examples_filepath": "nq_few_shot_prompt_for_judge_scoring.tsv",
    "llm_judge": "meta-llama/Llama-2-13b-hf", # Specify vLLM model
    "labels": ["Context_Relevance_Label"], 
    "gold_label_path": "nq_labeled_output.tsv",
    "vllm": True, # Toggle vLLM to True 
    "host_url": "http://0.0.0.0:8000/v1" # Replace with server hosting model followed by "/v1"
}

ares = ARES(ppi=ppi_config)
results = ares.evaluate_RAG()
print(results)

For more details, refer to our documentation.

Results Replication

We include synthetic datasets for key experimental results in synthetic_datasets. The few-shot prompts used for generation and evaluation are included in datasets. We also include instructions for fine-tuning LLM judges in the paper itself. Please reach out to jonsaadfalcon@stanford.edu or manihani@stanford.edu if you have any further questions.

Citation

To cite our work, please use the following Bibtex:

@misc{saadfalcon2023ares,
      title={ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems}, 
      author={Jon Saad-Falcon and Omar Khattab and Christopher Potts and Matei Zaharia},
      year={2023},
      eprint={2311.09476},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Appendix

Machine requirements and setup when not using OpenAI API

Machine requirements

• Over ~100 GB of available disk space
• GPU
  • Should work: A100 (e.g. Standard_NC24ads_A100_v4 on Azure)
  • Does not work:
    • Tested on 2023-12-17 with both Standard_NC6s_v3 and Standard_NC12s_v3, and ran into this error: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 160.00 MiB (GPU 0; 15.77 GiB total capacity; 15.12 GiB already allocated; 95.44 MiB free; 15.12 GiB reserved in total by PyTorch)

Machine setup

For example, on an Azure VM running Linux (ubuntu 20.04), you will need to do the following:

• Install conda
  • First set of commands (can copy-paste multiple lines)
    • wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
    • chmod +x Miniconda3-latest-Linux-x86_64.sh
    • ./Miniconda3-latest-Linux-x86_64.sh -b
  • Second set of commands (can copy-paste multiple lines)
    • export PATH="~/miniconda3/bin:$PATH"
    • conda init
• Install gcc
  • sudo apt-get -y update
  • sudo apt-get -y upgrade
  • sudo apt-get -y install build-essential
  • sudo apt-get -y install libpcre3-dev
• Install NVIDIA drivers
  • sudo apt install ubuntu-drivers-common -y
  • sudo ubuntu-drivers autoinstall
  • sudo reboot
  • SSH in again and confirm the installation was successful by running nvidia-smi
• cd to ARES folder and follow the rest of the README