Open-source alternatives to Metrics

Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for executing automated regression tests, validating model output quality against defined standards, and tracing the execution of complex agent workflows. By integrating these capabilities into development pipelines, the platform ensures consistent performance and reliability throughout the software lifecycle. The platform distinguishes itself through its focus on programmatic validation and observability. It utilizes secondary language models to score output quality and employs

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

This project is a standardized framework for benchmarking large language models across a wide range of academic and reasoning datasets. It provides a platform for executing automated evaluation tasks to measure model accuracy and performance, ensuring consistent assessment through a structured configuration schema. The framework distinguishes itself by incorporating a dedicated utility for data decontamination, which identifies and removes overlapping training samples from evaluation sets to prevent data leakage. It also features a flexible task builder that allows users to define custom benc

FlagEval, launched by BAAI in 2023, is a comprehensive large model evaluation system that encompasses over 800 open-source and closed-source models from around the globe. It features more than 40 capability dimensions, including reasoning, mathematical skills, and task-solving abilities, along…

CMMLU: Measuring massive multitask language understanding in Chinese

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

Sharing both practical insights and theoretical knowledge about LLM evaluation that we gathered while managing the Open LLM Leaderboard and designing lighteval!

Lighteval is an open-source framework for running standardized benchmarks and custom evaluation tasks against language models. It provides a system for defining new evaluation tasks with custom prompts, metrics, and scoring in YAML configuration files, and integrates with the Hugging Face Hub for storing and comparing results. The framework supports evaluating models across multiple inference backends, including transformers, vllm, and custom APIs, through a unified generation and log-probability interface. It includes a pluggable metric registry for built-in and custom scoring, a prediction

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

OpenCompass is a comprehensive evaluation platform, benchmarking suite, and distributed model evaluator designed to measure the performance and accuracy of large language models. It provides a framework for benchmarking both open-source and API-based models against diverse datasets using standardized metrics and reproducible pipelines. The project features an automated judging framework that uses language models as judges to score and verify the quality of generated text. It includes a performance leaderboard system for comparing the relative capabilities of various models across industry-sta

Context

PromptCBLUE: a large-scale instruction-tuning dataset for multi-task and few-shot learning in the medical domain in Chinese

Xiezhi (獬豸) is a comprehensive evaluation suite for Language Models (LMs). It consists of 249587 multi-choice questions spanning 516 diverse disciplines and four difficulty levels, as shown below. Please check our paper for more details, and our website will be open later on.

Automatic evals for LLMs

A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.

OpenCompass is an open-source framework for standardized benchmarking of large language models. It provides a configurable evaluation pipeline that supports both objective and subjective assessment, using a dual-engine architecture to handle closed-form answer comparison and open-ended response rating. The framework is designed as a modular platform where datasets, models, and metrics are composed through declarative YAML configuration files. The framework distinguishes itself through its extensible model integration layer, which supports custom models, HuggingFace models, and third-party API

VLMEvalKit is a vision-language model evaluation framework and inference engine designed to run standardized benchmarks and measure model accuracy across diverse visual datasets. It serves as a multimodal model benchmark and performance toolkit for calculating metrics and comparing model responses. The toolkit includes a specialized visual reasoning evaluator that uses adversarial samples to distinguish actual image understanding from reliance on language patterns. It also provides capabilities for image generation evaluation, testing a model's ability to create or modify visuals based on tex

GAOKAO-Bench is an evaluation framework that utilizes GAOKAO questions as a dataset to evaluate large language models.

LLM Comparator is an interactive data visualization tool for evaluating and analyzing LLM responses side-by-side, developed by the PAIR team.

The official evaluation suite and dynamic data release for MixEval.

This repository contains information about AGIEval, data, code and output of baseline systems for the benchmark.