Active Learning with AutoNLP and Prodigy

Machine Learning Experts - Sasha Luccioni

Reducing Human Annotation with ML Active Learning

In a world where human time is expensive, learn how to use it only when really necessary The post Reducing Human Annotation with ML Active Learning appeared first on Towards Data Science .

Machine Learning Experts - Lewis Tunstall

Neural Active Learning Meets the Partial Monitoring Framework

arXiv:2405. 08921v2 Announce Type: replace Abstract: We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors.

Cross-Domain Hybrid OPD for Generalizable Search Agents

Recent advances in Reinforcement Learning (RL) have substantially improved the capabilities of autonomous search agents, enabling sophisticated planning, and iterative retrieval over dynamic information sources. However, optimizing language models for specialized search behaviors often incurs an alignment tax, where gains in search performance come at the expense of general-purpose capabilities, limiting their effectiveness as universal assistants.

Activation-Based Active Learning for In-Context Learning: Challenges and Insights

arXiv:2606. 05134v1 Announce Type: cross Abstract: Deep active learning has previously been explored for LLM in-context sample selection, but not with methods that utilise recent advances in understanding of transformer activations.