Binary LLM routing with benchmark data

Repurpose benchmark datasets to train a router model as collection of binary classification tasks for LLM selection

CA clean typed decision with low volume or low value, or too vague to act on.3.00

Key facts

Vertical: Software & tech
Function: Agents & dev
Status: Seen in the wild
Volume: occasional
Value: meaningful
Risk: moderate
Evidence: described plan
Flags: None

Source: https://arxiv.org/html/2406.18665v2

Build this with a classifier

Define a typed decision with a bounded answer, then evaluate it on examples.

{
  "decision_type": "choice",
  "question": "Does this input match the decision in “Binary LLM routing with benchmark data”?",
  "input": "<input to classify>",
  "output": "one label from a fixed list"
}

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