oleval

oleval is an open-source evaluation framework designed to standardize the assessment of language models and other AI systems. It provides a modular toolkit for running, recording, and comparing evaluations across datasets and tasks. The project emphasizes reproducibility, interoperability, and extensibility, enabling researchers and practitioners to define task definitions, scoring rubrics, and reporting formats in a consistent way.

Key features include a library of evaluators for common automatic metrics such as BLEU, ROUGE, METEOR, and newer contextual metrics; support for human-in-the-loop scoring via rubric definitions; dataset loaders and task definitions; a plugin system for custom evaluators; and a command-line interface and Python API to integrate into experiments.

The architecture centers on core components: datasets, scorers, rubrics, evaluators, and experiment pipelines. Datasets provide standard interfaces for loading prompts, references, and model outputs; scorers compute scores; rubrics define criteria for human judgments; evaluators orchestrate automatic and human scoring; an experiment registry enables reproducible runs.

oleval supports single-task and multi-task evaluations, cross-domain comparison, and longitudinal assessment of model improvements. It allows researchers to craft task-specific rubrics to measure aspects such as factuality, consistency, helpfulness, and safety, and to aggregate results into comparison tables and visual reports.

Since its initial release, oleval has been maintained by an open community of researchers and practitioners. It has seen updates to expand metric coverage, improve performance, and enhance documentation. The project emphasizes transparency by including evaluation protocols and sample baselines to facilitate peer review.

See also: evaluation framework, natural language processing metrics, human evaluation in NLP.