AI BriefingBenchmarking Query Analysis Under High Cardinality Conditions
Key point
We benchmark the problem of handling high-cardinality categorical values that arises during LLM-based query analysis, and its solutions.
Details
When using an LLM to convert data into a structured format, accurately handling High Cardinality categorical values (Enums) is a very tricky challenge. When there are too many categorical values, the LLM may fail to fit all the values within its context, or it may cause increased cost, slower speed, and degraded reasoning ability, and even generate (hallucinate) values that don't actually exist.
To test this, this study built a dataset containing 10,000 author names and aliases. For example, this approach checks whether the system can correctly map a user's search for 'Harry Chase' to the actual author 'Harrison Chase'.
The experiment was limited to a Single LLM call, reflecting real-world search system conditions, and focused on measuring the following factors:
- Accuracy: Whether aliases are correctly mapped to actual values
- Latency: Response speed
- Cost: Economic efficiency based on token usage
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