Knowledge Tree
Relations (1)
related 2.00 — strongly supporting 3 facts
The human-in-the-loop paradigm is a methodology used to manage uncertainty and improve performance within the machine learning pipeline, as described in [1] and [2]. Furthermore, the relationship is formally established by academic literature that surveys these methodologies specifically within the field of machine learning, as noted in [3].
Facts (3)
Sources
Combining large language models with enterprise knowledge graphs frontiersin.org 3 facts
referenceThe paper 'A survey of human-in-the-loop for machine learning' by Wu et al. (2022) provides a comprehensive survey of human-in-the-loop methodologies within the field of machine learning.
claimThe human-in-the-loop (HITL) paradigm integrates human expertise into the modeling process to manage machine learning model uncertainty, as noted by Wu et al. (2022).
claimHuman-in-the-loop (HITL) methods effectively handle scarce or sparse data for Named Entity Recognition (NER) (Shen et al., 2017), address mislabeling (Muthuraman et al., 2021), and enhance data processing, model training, and inference stages of the machine learning pipeline (Zhang et al., 2019; Klie et al., 2020; Wu et al., 2022).