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Introduction
GenAI, a class of models capable of generating human-like outputs, is experiencing explosive growth. However, the lack of a rational approach to evaluating GenAI performance has given rise to Evaluation Derangement Syndrome (EDS). This article delves into the practical, business-driven perspective of EDS, analyzing its causes and consequences for GenAI development.
GenAI Evaluation in the Realm of the GPU-poor
GenAI lacks obvious and reliable quality monitoring tools, and the pressure to deliver quickly hinders thorough evaluation. Additionally, evaluation criteria vary from subjective to objective, posing technical difficulties. As a result, the lack of a rational, objective, and repetitive framework for evaluation is a common challenge for GPU-poor researchers.
Business Causes of EDS
EDS in the GPU-poor domain stems from the continuous hype-driven GenAI economy, rapid releases of models by the GPU-rich, and a lack of focus on combating EDS. There is immense pressure to ship fast and skip evaluation, which is not seen as a critical business goal.
Technical Causes of EDS
The technical obstacles to GenAI evaluation include the inadequacy of the ‘ground truth’ concept, innate subjectivity, extreme use-case specificity, diversity monitoring, and potential data leaks. These challenges make the evaluation of GenAI difficult.
How the Rich Manage EDS
The GPU-rich use Reinforcement Learning from Human Feedback (RLHF), leveraging preference models and automated feedback to train generative models. This approach makes them immune to EDS, but it is beyond the reach of the GPU-poor due to its high resource requirements.
Practical AI Solution
Consider leveraging Evaluation-Driven Development (EDD) to address EDS in GenAI. EDD involves creating and using cost-effective evaluation models tailored to specific use cases, allowing GPU-poor researchers to escape the constraints of EDS.
Stay tuned for the second part of this series, which will delve into the practicalities of EDD.
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