Evaluation in Language Model Development

When developing AI large language models for production, establishing an end-to-end evaluation workflow is essential. This process involves collecting failure or corner cases, refining settings based on insights into what works or doesn't, and ensuring the reliability and utility of model outputs across diverse inputs. Evaluations enable comparisons between different models and settings, facilitating the selection of an optimal configuration from both cost and quality perspectives. It's crucial to recognize that language models are highly sensitive to prompt changes, which can significantly impact performance metrics. Therefore, evaluations should be conducted after any modifications.

The evaluation process is akin to both unit and integration testing in software development. Unit testing should begin as soon as individual components are adjusted, while integration testing provides a comprehensive check on the system's overall functionality. Both forms of testing are crucial.

In the era of advanced foundation models outperforming human annotators, evaluation best practices are continuously evolving. An end-to-end evaluation acts as a critical indicator of whether your system will deliver accurate responses based on the available data and queries. Although initial manual inspection of queries and responses is valuable, shifting towards summary metrics or automated evaluations becomes necessary to handle an increasing number of edge cases effectively.

For accurate evaluation, a dataset of questions and answers—serving as a "gold standard"—is required. This dataset can be self-created or automatically generated by the Varex platform after data importation. Ensuring the dataset's accuracy is vital, as it significantly influences the overall results and decision-making process.