amazon-science/MEMERAG

amazon-science/MEMERAG

Description: MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation

Language: Python

License: NOASSERTION

Stars: 4

Forks: 0

Open issues: 5

Created: 2025-03-28T11:06:08Z

Pushed: 2026-02-11T14:53:06Z

Default branch: main

Fork: no

Archived: no

README:

MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation

Authors: María Andrea Cruz Blandón, Jayasimha Talur, Bruno Charron, Dong Liu, Saab Mansour, Marcello Federico

Overview

This repository contains MEMERAG dataset. Its intended uses are: 1. Model selection: Evaluate and compare different LLMs for their effectiveness as judges in the "LLM-as-a-judge" setting. 2. Prompt selection: Optimize prompts for LLMs acting as judges in RAG evaluation tasks.

This is a meta-evaluation benchmark. Data in the benchmark should not be used to train models.

Datasets

We provide three variants of the meta-evaluation datasets: 1. MEMERAG (data/memerag/): The original dataset 2. MEMERAG-EXT (data/memerag_ext/): Extended dataset with faithfulness and relevance labels from 5 human annotators 3. MEMERAG-EXT with Majority Voting (data/memerag_ext_w_majority_voting/): Contains labels obtained through majority voting over 5 annotations.

Each dataset provides RAG meta-evaluation support for English (EN), German (DE), Spanish (ES), French (FR), and Hindi (HI) and is provided in JSONL format with the following structure:

• query_id: unique identifier for a query. This is same as MIRACL query_id.
• query: The actual question asked
• context: Passages used for generating the answer
• answer: List of dictionaries containing
• sentence_id: The id of the sentence
• sentence: A sentence from the answer
• fine_grained_factuality: Fine grained faithfulness label
• factuality: Faithfulness label which is one of Supported, Not Supported and Challenging to determine
• relevance: Label representing answer relevance
• comments: Notes by annotators.

Setup

1. Create a new virtual environment with python 3.10

conda create -n yourenvname python=3.10

2. Install dependencies

pip install -r requirements.txt

Run the benchmark

The Python script in src/run_benchmark.py will run the automated evaluator for a specified prompt and language combination on the MEMERAG dataset.

Usage

python run_benchmark.py [arguments]

Required Arguments

--lang: Target language for evaluation (choices: 'en', 'es', 'de', 'fr', 'hi')
--dataset_name: The dataset you like to use. Valid values are `memerag` and `memerag_ext_w_majority_vote`
--model_id: LLM model to use (choices: "gpt4o_mini", "llama3_2-90b", "llama3_2-11b", "qwen_2_5-32B")
--sys_prompt_path: Path to system prompt template file. Sample system prompt can be found in the *prompts* directory
--task_prompt_path: Path to task prompt template file. Sample task prompt can be found in the *prompts* directory

Optional Arguments

--temperature: Temperature parameter for LLM (default: 0.1)
--top_p: Top-p sampling parameter (default: 0.1)
--bedrock_region: AWS region for Bedrock models
--aws_profile: AWS profile name
--num_proc: Number of parallel LLM API calls (default: 4)
--num_retries: Number of retry attempts for LLM calls (default: 6)

Supported Evaluator Models

1. GPT-4o Mini (`gpt4o_mini`)

• Requires setting the OPENAI_API_KEY as an environment variable.

2. Llama 3.2 (11B and 90B) Models

• llama3_2-90b and llama3_2-11b can be used via AWS Bedrock. Please provide appropriate AWS credentials in ~/.aws/credentials

3. Qwen 2.5 32B (`qwen_2_5-32B`)

• Can be used via the VLLM inference server.

Example

Run Llama 3.2 90B as an automatic evaluator with AG + COT prompt.

cd src

python run_benchmark.py --lang de --dataset_name memerag --model_id llama3_2-90b \
--sys_prompt_path prompts/ag_cot/sys_prompt.md --task_prompt_path prompts/ag_cot/task_prompt.md --num_proc 5

The script prints balanced accuracy and saves judgments to llm_judged_dataset.csv in the specified output directory.

Citation

If you found the benchmark useful, please consider citing our work.

@misc{Cruz2025,
title={MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation},
author={María Andrea Cruz Blandón and Jayasimha Talur and Bruno Charron and Dong Liu and Saab Mansour and Marcello Federico},
year={2025},
eprint={2502.17163},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.17163},
}