LLM Evaluation
Recently I gave a talk about LLM Evaluation at the research institution in Zurich, Switzerland
The goal is very pragmatic, I do not want to review all methods or benchmarks,
but review methods and benchmarks that are useful as examples or templates to build evaluation suites in practical industrial cases
I plan to rework it to make it more comprehensive and readable on its own, the current deck is intended to support my talk (I’ll record it and share the recording)
I’ll be grateful for any comments und feedback
To elucidate further, my perspective on the critical importance of evaluation stems from observing the historical competition among giants like Google, Microsoft Search, and Yahoo. During this era, each entity claimed superiority based on their proprietary evaluation metrics. Despite these claims, a stark difference in the quality of search results emerged, leading to a clear victor both in the United States and globally, attributed to unparalleled search quality. This victory underscores the role of robust evaluation systems.
Organizations employed talented scientists and engineers to design systems optimized against these evaluation metrics, creating what can be termed as ‘evaluation-driven systems.’ The essence of these systems lies in their iterative improvement process, where each update undergoes rigorous evaluation, and only enhancements that positively impact these metrics are implemented. Consequently, the long-term success of these systems is heavily contingent upon the effectiveness of their evaluation methodologies.
Google’s triumph can be partly attributed to its evaluation framework, which more accurately mirrored the quality of search results from a human perspective compared to its rivals. This was true for both offline and online evaluations. Addressing the challenge of devising a fair and effective evaluation, especially offline, remains a formidable task. Notably, traditional metrics like NDCG from the 1990s fall short in accurately assessing search engine performance today.
The narrative extends into the realm of Large Language Models (LLMs), which are predominantly consumer-facing and aim to tackle complex queries through interactive engagement. The evaluation of such systems is equally challenging yet pivotal. It shapes the development trajectory of these models and ultimately determines their success. Therefore, the task of evaluating LLMs holds paramount importance, demanding attention from both industry professionals and academic researchers. This conviction stems from the understanding that rigorous evaluation not only drives technological advancement but also ensures the alignment of these advancements with human needs and expectations.
