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Status: Unchecked ×Keyword: demonstration selection ×Clear all

2 claims from 1 paper

1. Computer Science › Machine Learning and Algorithms

   Active Learning Principles for In-Context Learning with Large Language Models

   Margatina, Schick, Αλέτρας and Dwivedi-Yu · 2023

   The paper treats demonstration selection for in-context learning as a one-round active learning problem and compares uncertainty, diversity and similarity methods across GPT and OPT models on 24 tasks.

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   1. UncheckedUncertainty sampling, which works well in conventional supervised learning, performs poorly when choosing demonstrations for in-context learning in large language models.“Notably, uncertainty sampling, despite its success in conventional supervised learning scenarios, performs poorly in this context.”
   2. UncheckedChoosing in-context examples with active learning methods favours high-quality examples that have low uncertainty and are similar to the test examples.“Our extensive experimentation involving a diverse range of GPT and OPT models across $24$ classification and multi-choice tasks, coupled with thorough analysis, unambiguously demonstrates that in-context example selection through AL prioritizes high-quality examples that exhibit low uncertainty and…”