Original Link: Llama 2: The New Open LLM SOTA (ft. Nathan Lambert, Matt Bornstein, Anton Troynikov, Russell Kaplan, Whole Mars Catalog et al.)
Summary
Introduction
In an episode of a podcast focusing on AI research and models, Alessio Fanelli hosts with guests Simon Willison and Nathan Lambert.
Alessio Fanelli reiterates the podcast's ongoing commitment to discussing AI topics, hinting at its increasing depth. He acknowledges Simon as a frequent guest and thanks Nathan, who has shared insights on the technical details of Lama two.
Simon Willison expresses his excitement about the release of LAMA two. He highlights that this version can be used for commercial purposes, marking a significant change from previous versions which were not. He also mentions that while the benchmarks suggest it's a quality product, time will be required to ascertain its real-world effectiveness. The model, although officially available through Meta's website upon form approval, has already seen unofficial distributions online.
Nathan Lambert, who is affiliated with Huggingface, shares his experience on the topic. He's a researcher involved in reinforcement learning from human feedback. He emphasizes the significance of LAMA two in terms of its research contribution. However, Nathan also points out that the paper's methodology is more evident than the specifics of the data sets used. This shift may be related to potential legal challenges regarding the training data used in the first LAMA. In the previous model, copyrighted data was involved, which may be one reason behind the decreased transparency in the new paper.
Matt Bornstein from a16z briefly mentions their version of evaluations, positioning it as more of a "feel-good" approach in contrast to the deep dives of other panelists.
The conversation revolves around the implications of LAMA two, its commercial viability, technical intricacies, and potential legal and ethical ramifications.