Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models. Snorkel AI is proud of its roots in the research community and endeavors to remain at the forefront…
Getting better performance from foundation models (with less data)
We’re taking a look at the research paper, LLMs can easily learn to reason from demonstration (Li et al., 2025), in this week’s community research spotlight. It focuses on how the structure of reasoning traces impacts distillation from models such as DeepSeek R1. What’s the big idea regarding LLM reasoning distillation? The reasoning capabilities of powerful models such as DeepSeek…
Learn how ARR improves QA accuracy in LLMs through intent analysis, retrieval, and reasoning. Is intent the key to smarter AI? Explore ARR results!
Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
We’re taking a look at the research paper, LLMs can easily learn to reason from demonstration (Li et al., 2025), in this week’s community research spotlight. It focuses on how the structure of reasoning traces impacts distillation from models such as DeepSeek R1. What’s the big idea regarding LLM reasoning distillation? The reasoning capabilities of powerful models such as DeepSeek…
Learn how ARR improves QA accuracy in LLMs through intent analysis, retrieval, and reasoning. Is intent the key to smarter AI? Explore ARR results!
Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
ROBOSHOT acts like a lens on foundation models and improves their zero-shot performance without additional fine-tuning.
Microsoft infrastructure facilitates Snorkel AI research experiments, including our recent high rank on the AlpacaEval 2.0 LLM leaderboard.
Humans learn tasks better when taught in a logical order. So do LLMs. Researchers developed a way to exploit this tendency called “Skill-it!”
Training large language models is a multi-layered stack of processes, each with its unique role and contribution to the model’s performance.
Low-rank adaptation (LoRA) lets data scientists customize GenAI models like LLMs faster than traditional full fine-tuning methods.
Snorkel researchers’ state-of-the-art methods created a 7B LLM that ranked 2nd, behind only GPT-4 Turbo, on AlpacaEval 2.0 leaderboard.
Snorkel CEO Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG).
Snorkel CEO Alex Ratner chatted with Stanford Professor Percy Liang about evaluation in machine learning and in AI generally.
The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models. Snorkel AI is proud of its roots in the research community and endeavors to remain at the forefront…
Distillation techniques allow enterprises to access the full predictive power of large language models at a tiny fraction of their cost.
Gideon Mann, head of ML Product and Research at Bloomberg LP, chatted with Snorkel CEO Alex Ratner about building BloombergGPT.
Professionals in the data science space often debate whether RAG or fine-tuning yields the better result. The answer is “both.”