#AI#GenerativeAI#LLMs#ParetoCurves: "Which is the most accurate AI system for generating code? Surprisingly, there isn’t currently a good way to answer questions like these.
Based on HumanEval, a widely used benchmark for code generation, the most accurate publicly available system is LDB (short for LLM debugger).1 But there’s a catch. The most accurate generative AI systems, including LDB, tend to be agents,2 which repeatedly invoke language models like GPT-4. That means they can be orders of magnitude more costly to run than the models themselves (which are already pretty costly). If we eke out a 2% accuracy improvement for 100x the cost, is that really better?
In this post, we argue that:
AI agent accuracy measurements that don’t control for cost aren’t useful.
Pareto curves can help visualize the accuracy-cost tradeoff.
Current state-of-the-art agent architectures are complex and costly but no more accurate than extremely simple baseline agents that cost 50x less in some cases.
Proxies for cost such as parameter count are misleading if the goal is to identify the best system for a given task. We should directly measure dollar costs instead.
Published agent evaluations are difficult to reproduce because of a lack of standardization and questionable, undocumented evaluation methods in some cases."