Evaluation Pipeline

The evaluation pipeline is the central orchestration mechanism in FreeEval, designed to coordinate the execution of evaluation steps on models using specific datasets. It provides a structured framework for running comprehensive assessments of language model capabilities.

Pipeline Architecture

At its core, an evaluation pipeline manages the flow of information between three primary components: models, datasets, and evaluation steps. When you execute a pipeline, it systematically applies each evaluation step to your selected models using the specified datasets, collecting results throughout the process.

The pipeline maintains a shared context that evolves as steps are executed, allowing information to be passed between steps and enabling more sophisticated evaluation sequences. This context-aware design supports complex evaluation workflows where earlier steps might influence the behavior of later steps.

Pipelines are highly composable - you can combine multiple steps in a single pipeline to evaluate different aspects of model performance. For example, you might chain together a basic evaluation step with factuality assessment and interactive dialogue evaluation to gain a comprehensive understanding of a model’s capabilities.

Pipeline Execution

When a pipeline runs, it follows a predictable flow:

First, the pipeline initializes the evaluation context and prepares any necessary resources. It then executes each step in sequence, providing the step with access to the models, datasets, and current context. As each step completes, it contributes its results to the shared context and may modify the state to influence subsequent steps.

After all steps have been executed, the pipeline collects and organizes the results, making them available for analysis, visualization, or export. This structured approach ensures that evaluations are reproducible and results are consistently organized.

The pipeline handles necessary cleanup operations, such as releasing model resources or closing connections, ensuring efficient resource management throughout the evaluation process.

Building Custom Pipelines

FreeEval’s pipeline architecture is flexible by design, allowing you to create custom evaluation workflows tailored to your specific requirements. You can select which models to evaluate, which datasets to use, and which evaluation steps to include.

This flexibility enables various evaluation scenarios, from simple benchmarking of a single model against a standard dataset to comparative analysis of multiple models across diverse evaluation criteria. The pipeline abstraction allows you to focus on what you want to evaluate rather than how to implement the evaluation mechanics.

By understanding the pipeline concept, you can leverage FreeEval’s architecture to create sophisticated evaluation workflows that provide deep insights into language model performance across a variety of tasks and metrics.