Home-made Evals Just Taste Better
Note This is a shortened version of the original writing on kig.re — my personal blog. Here is what a real lead typed into a tax-preparation intake form: “Got married in June, haven’t figured out how we’re filing yet. There’s an IRS letter somewhere in a kitchen drawer I haven’t opened. I also deliver groceries on weekends.” Only one fact in that paragraph is clear: the weekend side gig. A model…
The source material discusses the importance of rigorous evaluation in assessing language models for practical applications, using the example of tax preparation.
It highlights a flawed prompt where GPT-4o-mini hallucinated a filing status of "married_jointly" when the correct answer was null, indicating a failure to capture the details provided by the user. The author built a tool called llm-evals to evaluate models consistently across various metrics such as accuracy, hallucinations, errors, latency, and cost, allowing for a weighted scoring system to determine the best model for a specific use case.
The author emphasizes that while store-bought leaderboards may prioritize certain metrics like accuracy or cost, they do not necessarily align with the specific needs of a particular application. The llm-evals suite provides a customizable framework to evaluate models based on the unique failure modes and priorities of each use case, ensuring that the chosen model provides the most accurate and reliable results.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
