•1 min read•from Machine Learning
Why is human LLM annotation so expensive? [D]
Our take
Human LLM annotation can be prohibitively expensive, particularly when relying on services like Scale AI, which emphasize quality and domain expertise. While platforms like MTurk offer lower costs, they often fall short in delivering the accuracy needed for specialized tasks. This creates a challenge for small teams requiring a few thousand labeled examples to refine evaluations or fine-tune models. Finding a balance between quality and cost is essential. How are others navigating this landscape? Are you managing annotations manually, or have you discovered effective solutions?
Scale AI and similar services charge a lot for annotation. MTurk is cheap but the quality is horrible for anything requiring real domain understanding.
For small teams that need a few thousand labeled examples to calibrate their evals or fine tune a model, there seems to be no good middle ground.
How is everyone handling this? Are you doing it manually or has anyone found something that actually works?
[link] [comments]
Read on the original site
Open the publisher's page for the full experience
Tagged with
#natural language processing for spreadsheets#generative AI for data analysis#Excel alternatives for data analysis#rows.com#real-time data collaboration#real-time collaboration#human LLM annotation#domain understanding#Scale AI#labeled examples#annotation#expensive#data annotation#MTurk#model#cost#machine learning#quality#fine tune#middle ground