HERMES introduces hierarchical labeling for AI pre-training data

Researchers have developed HERMES, a novel labeling substrate designed to improve pre-training data mixtures for AI models. Unlike existing methods that rely on fixed semantic axes or granularities, HERMES offers a hierarchical system derived from the data itself. This allows for flexible control over granularity, enabling more nuanced data mixture designs and potentially uncovering interactions between data quality and coverage that fixed-granularity pipelines cannot test. AI

IMPACT This research could lead to more effective AI model pre-training by enabling finer control over data mixtures and uncovering new insights into data quality interactions.

RANK_REASON The item is an academic paper detailing a new method for AI data labeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• HERMES
• Hugging Face
• IArxiv
• k-means clustering
• ScienceCast

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →