NLP Benchmarks and Reasoning in LLMs
Analysis
This article summarizes a podcast episode discussing NLP benchmarks, the impact of pretraining data on few-shot reasoning, and model interpretability. It highlights Yasaman Razeghi's research showing that LLMs may memorize datasets rather than truly reason, and Sameer Singh's work on model explainability. The episode also touches on the role of metrics in NLP progress and the future of ML DevOps.
Key Takeaways
- •LLMs may rely on memorization rather than true reasoning.
- •Accuracy in reasoning tasks can be correlated to term frequency in the training data.
- •Model interpretability is crucial for understanding and improving ML models.
- •The role of metrics in NLP progress is questioned.
Reference
“Yasaman Razeghi demonstrated comprehensively that large language models only perform well on reasoning tasks because they memorise the dataset. For the first time she showed the accuracy was linearly correlated to the occurance rate in the training corpus.”