Don’t classify. Hallucinate!
A useful pattern for LLM classification at scale is to let it hallucinate plausible, fake entities. Then resolve to real ones.
ML Engineering Article
Summary
A technique for achieving deterministic classification from stochastic LLM outputs by clustering generated labels using vector embeddings and disjoint set union, with a benchmarked Golang implementation for large-scale tweet classification.
Highlights
- LLMs produce inconsistent lexical labels for identical inputs, but these labels are often semantically similar
- Clustering labels via vector embeddings and disjoint set union reduces unique labels from linear to sublinear growth
- The vectorization approach is initially slower and more expensive but scales to become significantly cheaper at large volumes
- Use case: classifying tens of thousands of tweets daily with an open-ended label space
- Golang implementation with benchmarks for accuracy, cost, and latency is provided
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Context
Audience
ML engineers, data engineers, and developers building LLM-powered classification pipelines at scale
DomainLLM Engineering & Data Labeling
Formatlong-form technical article
Accessfree online
Source Overview
LLMs generate inconsistent lexical labels for identical inputs, but these labels are often semantically similar.
Clustering generated labels using vector embeddings and disjoint set union enables consistent classification, reducing unique labels from linear to sublinear growth as dataset size increases. This vectorization approach is initially slower and slightly more expensive, but scales to become significantly cheaper and faster than pure LLM-only classification at large volumes.
Topics
Related
Vector embeddings for text clusteringDisjoint set union / Union-Find algorithmLLM prompt engineering for classificationlogit_bias and json_schema constraintsAndrew Ng - Opportunities in AI (Stanford 2023)
