Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve
competitive results in Text Classification tasks. In-Context Learning (ICL)
typically achieves better accuracy than the 0-shot setting, but it pays in
terms of efficiency, due to the longer input prompt. In this paper, we propose
a strategy to make LLMs as efficient as 0-shot text classifiers, while getting
comparable or better accuracy than ICL. Our solution targets the low resource
setting, i.e., when only 4 examples per class are available. Using a single LLM
and few-shot real data we perform a sequence of generation, filtering and
Parameter-Efficient Fine-Tuning steps to create a robust and efficient
classifier. Experimental results show that our approach leads to competitive
results on multiple text classification datasets.

提出了一种方法，使大型语言模型在 0-shot 文本分类任务中成为高效的文本分类器，并在低资源环境下获得了竞争性结果。

使用 PEFT 和合成数据增强低资源 LLMs 分类

Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data

Question Answering (QA) systems require a large amount of annotated data
which is costly and time-consuming to gather. Converting datasets of existing
QA benchmarks are challenging due to different formats and complexities. To
address these issues, we propose an algorithm to automatically generate shorter
questions resembling day-to-day human communication in the Natural Questions
(NQ) dataset from longer trivia questions in Quizbowl (QB) dataset by
leveraging conversion in style among the datasets. This provides an automated
way to generate more data for our QA systems. To ensure quality as well as
quantity of data, we detect and remove ill-formed questions using a neural
classifier. We demonstrate that in a low resource setting, using the generated
data improves the QA performance over the baseline system on both NQ and QB
data. Our algorithm improves the scalability of training data while maintaining
quality of data for QA systems.

本研究提出一种算法，利用数据集转换技术从长的 Trivia 问题转化为类似于日常人类交流的较短问题的方式，自动生成自然问题（NQ）数据集中的问题，同时使用神经分类器检测并去除不合法的问题，从而生成高质量的数据集，提高了 QA 表现，该算法在低资源环境下使用，扩展了 QA 系统的规模，同时保持了训练数据的质量。