Happy holidays! We’ll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production! For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver. Today, we’re proud to share Loubna’s highly anticipated talk (slides here)!

Synthetic Data We called out the Synthetic Data debate at last year’s NeurIPS, and no surprise that 2024 was dominated by the rise of synthetic data everywhere: * Apple’s Rephrasing the Web, Microsoft’s Phi 2-4 and Orca/AgentInstruct, Tencent’s Billion Persona dataset, DCLM, and HuggingFace’s FineWeb-Edu, and Loubna’s own Cosmopedia extended the ideas of synthetic textbook and agent generation to improve raw web scrape dataset quality * This year we also talked to the IDEFICS/OBELICS team at HuggingFace who released WebSight this year, the first work on code-vs-images synthetic data. * We called Llama 3.1 the Synthetic Data Model for its extensive use (and documentation!) of synthetic data in its pipeline, as well as its permissive license. * Nemotron CC and Nemotron-4-340B also made a big splash this year for how they used 20k items of human data to synthesize over 98% of the data used for SFT/PFT. * Cohere introduced Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress observing gains of up to 56.5% improvement in win rates comparing multiple teachers vs the single best teacher model * In post training, AI2’s Tülu3 (discussed by Luca in our Open Models talk) and Loubna’s Smol Talk were also notable open releases this year. This comes in the face of a lot of scrutiny and criticism, with Scale AI as one of the leading voices publishing AI models collapse when trained on recursively generated data in Nature magazine bringing mainstream concerns to the potential downsides of poor quality syndata: Part of the concerns we highlighted last year on low-background tokens are coming to bear: ChatGPT contaminated data is spiking in every possible metric: