weakly-supervised-learning

The paper introduces a weakly supervised framework using large language models to extract dataset mentions in forced displacement and FCV documents, achieving high accuracy with limited labeled data.

This chapter reviews recent advances in weakly supervised learning, introducing confidence-difference classification, relaxed assumptions for complementary-label learning, and an evaluation framework for partial-label learning.

This paper proposes a two-stage approach for early failure alerting in dialogs and LLM-agent trajectories, addressing the challenge of sparse evidence by learning turn-level failure evidence from trajectory labels and using an attention-based predictor with a preference-conditioned stopping policy (α-STOP) to achieve controllable accuracy-earliness trade-offs.