On the recordRead 9 Oct 202622 facts
ARES
On the record
- Annotation guidance
- The README recommends at least 50 annotated query, document, and answer examples for the human preference validation set, with several hundred ideal.github.com · read 4 Oct 2026
- Compute requirements
- For execution without the OpenAI API, the README lists over about 100 GB of available disk space and a GPU, and says an A100 should work.github.com · read 9 Oct 2026
- Custom systems
- ARES is model-agnostic and can evaluate queries and answers generated by custom RAG models.ares-ai.vercel.app · read 4 Oct 2026
- Data requirements
- ARES requires an in-domain prompts dataset and an unlabeled evaluation set; a labeled evaluation set is optional because PPI can create one using machine labels.ares-ai.vercel.app · read 4 Oct 2026
- Dataset example
- The README says the optional full Natural Questions dataset is 37.3 GB.github.com · read 9 Oct 2026
- Datasets
- ARES can retrieve KILT datasets including nq, hotpotqa, wow, and fever, and SuperGLUE datasets including record, rte, boolq, and multirc.ares-ai.vercel.app · read 4 Oct 2026
- Evaluation criteria
- ARES assesses context relevance, answer faithfulness, and answer relevance.github.com · read 9 Oct 2026
- Evaluation method
- ARES combines synthetic data generation, fine-tuned classifiers, and Prediction-Powered Inference to estimate evaluation results with statistical confidence.github.com · read 9 Oct 2026
- Evaluation metrics
- ARES evaluates context relevance, answer faithfulness, and answer relevance.github.com · read 4 Oct 2026
- Hardware requirements
- The README says local execution needs over about 100 GB of available disk space and a GPU, and names an A100 as a working example.github.com · read 4 Oct 2026
- Input requirements
- The README says evaluation uses a human preference validation set, few-shot examples, and a larger set of unlabeled query-document-answer triples; it recommends at least 50 validation examples.github.com · read 9 Oct 2026
- Install
- The documentation provides installation through `pip install ares-ai` or by cloning the GitHub repository and installing it locally.ares-ai.vercel.app · read 4 Oct 2026
- Installation
- The README gives `pip install ares-ai` as the installation command.github.com · read 9 Oct 2026
- Intended users
- The project documentation describes ARES as a tool for evaluating RAG systems and comparing different RAG configurations.github.com · read 9 Oct 2026
- License
- The GitHub repository identifies its license as Apache-2.0.github.com · read 9 Oct 2026
- Local execution
- ARES supports running models locally with vLLM, which the README says can enable offline operation and enhanced privacy.github.com · read 9 Oct 2026
- Method
- ARES generates synthetic training data, fine-tunes lightweight language models as judges, and uses prediction-powered inference with human-annotated examples to produce confidence intervals.ares-ai.vercel.app · read 4 Oct 2026
- Model integrations
- The README gives examples using OpenAI and TogetherAI API keys and shows local model execution with vLLM.github.com · read 4 Oct 2026
