open-compass/opencompass

Features

• LLM Evaluation Frameworks - Provides an open-source framework for standardized benchmarking of large language models across diverse capabilities and datasets.
• AI API Adapters - Provides a common adapter layer for wrapping third-party API models with authentication and request formatting.
• Automated Model Judges - Scores open-ended model outputs using automated judge models or human-like rating scales for quality assessment.
• Benchmarking Suites - Provides a comprehensive evaluation suite covering examination, reasoning, language, knowledge, and safety dimensions for LLMs.
• Custom Model Integrations - Extends the evaluation framework to support user-defined models, third-party APIs, or HuggingFace models through a plug-in interface.
• Evaluation Engines - Operates two parallel evaluation engines for closed-form answer comparison and open-ended response rating.
• Generative Model Evaluation - Compares model outputs to standard answers using discriminative and generative methods with prompt engineering.
• LLM Benchmarking - Runs standardized evaluation suites across multiple model types and datasets from a single configuration file.
• Model Capability Assessment - Measures model performance across examination, knowledge, reasoning, understanding, language, and safety dimensions.
• Extension Interfaces - Ships a base class interface that new models implement to integrate with the evaluation pipeline.
• Model Benchmarking Suites - Runs predefined evaluation datasets against a model by executing a single configuration file.
• Model Evaluation Frameworks - Integrates user-defined models into the evaluation pipeline by following the framework's extension interface.
• Dual-Engine Evaluation Pipelines - Provides a configurable pipeline for running objective and subjective evaluations on both open-source and API-based language models.
• Model Prediction Evaluation - Compares model predictions against ground-truth answers using discriminative and generative methods with prompt engineering.
• LLM Capability Dimensions - Assesses model performance across examination, knowledge, reasoning, understanding, language, and safety dimensions.
• Third-Party Model Integration - Supports integrating third-party, HuggingFace, and API-based models through a common subclassing interface.
• Chat Model Evaluations - Ships structured tests for evaluating conversational ability and human alignment of instruction-tuned chat models.
• Configuration-Driven Orchestrators - Defines the entire evaluation workflow in a single YAML file specifying datasets, models, judges, and output paths.
• Pipeline Component Modularization - Composes evaluation tasks by chaining configurable dataset, model, and metric modules through a declarative YAML pipeline.
• LLM-As-A-Judge Scoring - Delegates subjective scoring to a separate LLM judge configured as a pluggable evaluator module.
• Agent Evaluation Tools - Measures a model's ability in complex tool calling and code interpreter usage for data science and mathematics.
• Domain Knowledge Evaluations - Measures model knowledge across science, engineering, and humanities using objective evaluation.
• Dataset-Scoped Metrics - Allows each dataset to declare its own scoring function and post-processing steps for custom evaluation.
• Satisfaction Score Collectors - Collects human or model-simulated satisfaction scores for open-ended responses to gauge real-world quality.
• Instruction Following Evaluations - Provides subjective evaluations measuring how accurately models follow complex instructions.
• API-Deployed Evaluations - Deploys a downloaded model as an API service and runs benchmark evaluations against it.
• HuggingFace Evaluations - Configures a HuggingFace model for evaluation by specifying its path, tokenizer, and inference parameters.
• Per-Dataset Metric Configurators - Allows configuring custom scoring functions and post-processing steps for each evaluation dataset.
• API Service Evaluations - Connects to third-party API services such as OpenAI or ChatGLM to run model evaluations.
• Visual Data Reasoning Evaluation - Measures logical, common-sense, and tabular reasoning skills through subjective evaluation.
• LLM Language Proficiency Evaluations - Measures model ability in information extraction, summarization, dialogue, and creative writing using subjective evaluation.
• Mathematical Reasoning Evaluations - Measures numerical computation and problem-solving skills at high school and university levels using objective evaluation.
• API Model Integration - Extends the evaluation framework by subclassing a base API model and implementing generation and token-length methods.
• Code Generation Evaluators - Measures code generation correctness and quality using both objective and subjective evaluation methods.
• Model Evaluation - Comprehensive platform for evaluating models across numerous datasets.
• Model Evaluation and Benchmarking - Evaluation platform supporting a wide range of foundation models.
• Natural Language Processing - Listed in the “Natural Language Processing” section of the FunNLP awesome list.
• Evaluation Benchmarks - Comprehensive evaluation platform for language and multimodal models.

Star-Verlauf