AI BriefingDon't Classify, Hallucinate!
Key point
This article introduces an efficient technique that induces hallucinations instead of using structured outputs from LLMs, then performs actual classification via vector similarity.
Details
When classifying products or search queries, using Structured Outputs with hundreds of categories can be costly and may face token limit issues.
Instead, leveraging the 'Hallucination Pattern' allows for much cheaper and more efficient classification tasks.
Core Process:
- Induce Hallucination: Ask a cheap, small LLM to generate 'plausible fake category names' suitable for the query. There is no need to include the actual category list in the prompt.
- Generate Embeddings: Maintain a pre-computed set of Embeddings for the actual category list.
- Similarity Matching: Compute the embedding of the fake category name generated by the LLM, then perform a Dot Product with the actual category embeddings to find the most similar real value.
Using this approach eliminates the need to pass large-scale taxonomies directly to the model, enabling economically viable large-scale classification tasks even with Small/Dumb models.
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