Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
134 tokens/sec
GPT-4o
9 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification (2204.06305v2)

Published 13 Apr 2022 in cs.CL, cs.AI, and cs.LG

Abstract: Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained LLM. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method to automatically select label mappings for few-shot text classification with prompting. Our method exploits one-to-many label mappings and a statistics-based algorithm to select label mappings given a prompt template. Our experiments demonstrate that AMuLaP achieves competitive performance on the GLUE benchmark without human effort or external resources.

Citations (11)

Summary

We haven't generated a summary for this paper yet.