Building the Ed-O-Meter: Notes on Writing My Own LLM Benchmark (reinvently.co.uk)

The new benchmark tool, Featherbench, has been developed to rigorously evaluate large language models (LLMs) through a consistent set of real-world tasks. Unlike traditional evaluation suites that may optimize for specific areas external to practical applications, Featherbench emphasizes direct comparisons of model performance on varied tasks relevant to everyday use, including coding, data handling, and security checks. This system allows users to run multiple models through 28 fixed tasks while controlling for variables, ensuring reproducibility and transparency in results. Notably, it counts refusals from models as failures, reflecting the practical challenges users face in production environments where the performance and reliability of LLMs can be closely tied to cost and efficiency. This development is significant for the AI/ML community as it highlights a shift towards evaluating model capabilities in ways that align with actual user needs rather than previous benchmarks that might favor certain metrics over practical utility. The tool supports a well-grounded, objective approach to comparison, revealing insights into model behaviors that conventional leaderboards often overlook. The publication of detailed results, including a refusal breakdown, encourages transparency and paves the way for a more nuanced understanding of LLM strengths and weaknesses, especially in light of diverging behaviors among models in an increasingly commoditized market.