Analysis
Exciting research is revealing the direct correlation between AI model size and its core reasoning abilities. The study uses a 'reasoning-free' test to strip away the 'Chain of Thought' and assess the fundamental capabilities of different Large Language Models. This work is a fascinating look at the 'scaling laws' that govern LLM performance.
Key Takeaways
- •A study assessed LLMs by removing their ability to use 'Chain of Thought' reasoning.
- •The research reveals a strong correlation between the size of the model and its reasoning ability, even on elementary school math problems.
- •By using scaling laws, the study allows for the estimation of the parameters of closed-source models.
Reference / Citation
View Original"The most interesting point of this experiment is the fact that the 'accuracy rate without thought' and the 'number of model parameters (brain size)' draw an extremely clean logarithmic linear graph (log-linear)."