We develop methods, benchmarks, and training systems that turn expert data into frontier AI

Snorkel CEO Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG).

Labeling training data is a critical and expensive step in producing high accuracy ML models, whether training from scratch or fine-tuning. To make labeling more efficient, two major approaches are programmatic weak supervision (WS) and semi-supervised learning (SSL). More recent works have either explicitly or implicitly used techniques at their intersection, but in various complex and ad hoc ways. In…

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data obtained from humans is used to find a confidence threshold above which the data is machine-labeled, reduces reliance on manual annotation. TBAL is emerging as a widely-used solution in practice. Given the long shelf-life and diverse usage of the resulting…

Snorkel CEO Alex Ratner chatted with Stanford Professor Percy Liang about evaluation in machine learning and in AI generally.

Honeypots are a classic cyber-deceptive technique that allows a defender to add false information into the system in an effort to deter/delay/distract potential attackers. However, the effectiveness of honeypots is dependent on their design along with the environment into which they are deployed. In this work, we consider the scenario where there is a collection of honeypots along with a…

Zero-shot inference is a powerful paradigm that enables the use of large pretrained models for downstream classification tasks without further training. However, these models are vulnerable to inherited biases that can impact their performance. The traditional solution is fine-tuning, but this undermines the key advantage of pretrained models, which is their ability to be used out-of-the-box. We propose ROBOSHOT, a…

Organizations typically train large models individually. This is costly and time-consuming, particularly for large-scale foundation models. Such vertical production is known to be suboptimal. Inspired by this economic insight, we ask whether it is possible to leverage others’ expertise by trading the constituent parts in models, i.e., sets of weights, as if they were market commodities. While recent advances in…

Compressing large language models (LLMs), often consisting of billions of parameters, provides faster inference, smaller memory footprints, and enables local deployment. Two standard compression techniques are pruning and quantization, with the former eliminating redundant connections in model layers and the latter representing model parameters with fewer bits. The key tradeoff is between the degree of compression and the impact on…

We present Bonito, the first open-source model for conditional task generation: the problem of converting unannotated corpus into a collection of tasks for instruction tuning. Our goal is to enable efficient task adaptation of instruction tuned language models on users’ specialized, private data without relying on proprietary API-access-only models like GPT-4. We create Bonito by remixing existing, general-purpose instruction tuning…