FLEX: Unifying Evaluation for Few-Shot NLP

TL;DR

The paper introduces the FLEX principles for rigorous few-shot NLP evaluation, presents a comprehensive benchmark and leaderboard, and proposes UniFew, a simple yet competitive prompt-based model unifying pretraining and finetuning.

Contribution

It establishes unified evaluation principles, releases a diverse benchmark, and proposes UniFew, a straightforward prompt-based model that matches complex approaches.

Findings

FLEX principles improve evaluation consistency and rigor.

UniFew achieves competitive results with simpler design.

Benchmark covers diverse NLP tasks with a public leaderboard.

Abstract

Few-shot NLP research is highly active, yet conducted in disjoint research threads with evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful experimental design. Consequently, the community does not know which techniques perform best or even if they outperform simple baselines. In response, we formulate the FLEX Principles, a set of requirements and best practices for unified, rigorous, valid, and cost-sensitive few-shot NLP evaluation. These principles include Sample Size Design, a novel approach to benchmark design that optimizes statistical accuracy and precision while keeping evaluation costs manageable. Following the principles, we release the FLEX benchmark, which includes four few-shot transfer settings, zero-shot evaluation, and a public leaderboard that covers diverse NLP tasks. In addition, we present UniFew, a prompt-based model for few-shot learning that unifies pretraining and finetuning prompt formats, eschewing complex machinery of recent prompt-based approaches in adapting downstream task formats to language model pretraining objectives. We demonstrate that despite simplicity, UniFew achieves results competitive with both popular meta-learning and prompt-based approaches.