Have you ever stopped to think about how we evaluate the performance of Large Language Models (LLMs)? It’s a bit mind-boggling, really. We’re using AI to judge AI, which has led to a messy feedback loop. But there might be a way out of this cycle.
The problem is that when we want to validate an LLM, we use another LLM to evaluate it. It’s like asking a student to grade their own homework – except the student is also grading everyone else’s homework too. I’ve been running experiments, and the results are concerning.
For one, evaluating large datasets with LLMs is expensive. The results are also inconsistent, with the same input producing wildly different outputs. Smaller models produce garbage, and manual validation is still needed. It’s clear that we need a better approach.
Even the big players are stuck in this loop. I recently watched a presentation by Mistral.AI, where they admitted to relying on LLM-as-judge to validate their models. Their ‘gold standard’ is manual validation, but they can only afford it for one checkpoint.
But there’s hope. I stumbled upon a research project called TruthEval that’s trying to break out of this cycle. They generate corrupted datasets to test whether LLM-as-judge can actually catch errors. The results show that other methods are more reliable than LLM-as-judge.
This isn’t just about evaluation; it’s about the entire AI ecosystem. We’re building systems that validate themselves, and when they fail, we use more of the same broken approach to fix them. It’s time to rethink our approach and find a way out of this feedback loop.
So, how do we break out of this cycle? Are there better evaluation methods we’re missing? Should we be focusing more on human-in-the-loop validation? Or is there a completely different approach we should be exploring?
I’m curious to hear your thoughts. Are you seeing the same issues in your work?