Recent attempts to replicate the influential Chinchilla scaling paper by Hoffmann et al., which is central to the language modeling community, have revealed significant discrepancies. Researchers tried to replicate the paper's parametric scaling law and found issues with the original fitting of the model. A reanalysis and reconstruction of the data led to different, and reportedly better, results. This has prompted calls for methodological refinement and validation of compute-optimal scaling laws for large language models (LLMs).
Nice analysis. I think this resolves why approach 3 didn't match 1 & 2. Also I am seeing people share this paper and suggest this is proves scaling laws don't exist. My take on their findings: now 3 out of 3 approaches are in agreement instead of 2 out of 3. https://t.co/xBxZKnIBwg
Nice analysis. I think this resolves why approach 3 didn't match 1 & 2. Also I am seeing people share this paper and proclaiming this is evidence that scaling laws don't exist. My take on their findings: now 3 out of 3 approaches are in agreement instead of 2 out of 3. https://t.co/xBxZKnIBwg
Reanalysis of Hoffmann et al.'s compute-optimal scaling laws for LLMs shows significant deviations & suggests need for methodological refinement & validation: https://t.co/Q7who9J7e3 https://t.co/iPzrcLNXoT
tl;dr: the parametric Chinchilla scaling law appears to have been poorly fit, undermining any analysis that relied on its exact fitted values. We fit the same scaling law to a reconstruction of their data, getting different and IMO better results. https://t.co/52JwuGNmYm https://t.co/cSUycuId3u
We attempted to replicate the Chinchilla paper's parametric scaling law, and we found some issues. https://t.co/wnKXEgC7aX
The Chinchilla scaling paper by Hoffmann et al. has been highly influential in the language modeling community. We tried to replicate a key part of their work and discovered discrepancies. Here's what we found. (1/9) https://t.co/BFOP70Aj0W
Chinchilla Scaling: A replication attempt https://t.co/ZZxldsnJWO