RAFT: A Real-World Few-Shot Text Classification Benchmark. (arXiv:2109.14076v3 [cs.CL] UPDATED)

Large pre-trained language models have shown promise for few-shot learning,
completing text-based tasks given only a few task-specific examples. Will
models soon solve classification tasks that have so far been reserved for human
research assistants? Existing benchmarks are not designed to measure progress
in applied settings, and so don't directly answer this question. The RAFT
benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring
tasks and uses an evaluation setup that mirrors deployment. Baseline
evaluations on RAFT reveal areas current …

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