Retrained large language models (LLMs) have become extensively used across
various sub-disciplines of natural language processing (NLP). In NLP, text
classification problems have garnered considerable focus, but still faced with
some limitations related to expensive computational cost, time consumption, and
robust performance to unseen classes. With the proposal of chain of thought
prompting (CoT), LLMs can be implemented using zero-shot learning (ZSL) with
the step by step reasoning prompts, instead of conventional question and answer
formats. The zero-shot LLMs in the text classification problems can alleviate
these limitations by directly utilizing pretrained models to predict both seen
and unseen classes. Our research primarily validates the capability of GPT
models in text classification. We focus on effectively utilizing prompt
strategies to various text classification scenarios. Besides, we compare the
performance of zero shot LLMs with other state of the art text classification
methods, including traditional machine learning methods, deep learning methods,
and ZSL methods. Experimental results demonstrate that the performance of LLMs
underscores their effectiveness as zero-shot text classifiers in three of the
four datasets analyzed. The proficiency is especially advantageous for small
businesses or teams that may not have extensive knowledge in text
classification.

利用零样本学习采用递进性思维提示，与传统的问答格式相比，GPT 模型在文本分类问题上具备零样本分类器的能力，有效地利用提示策略在各种文本分类场景中展现出较好的性能。

大型语言模型的零样本文本分类器

Large Language Models Are Zero-Shot Text Classifiers

How to usefully encode compositional task structure has long been a core
challenge in AI. Recent work in chain of thought prompting has shown that for
very large neural language models (LMs), explicitly demonstrating the
inferential steps involved in a target task may improve performance over
end-to-end learning that focuses on the target task alone. However, chain of
thought prompting has significant limitations due to its dependency on huge
pretrained LMs. In this work, we present compositional fine-tuning (CFT): an
approach based on explicitly decomposing a target task into component tasks,
and then fine-tuning smaller LMs on a curriculum of such component tasks. We
apply CFT to recommendation tasks in two domains, world travel and local
dining, as well as a previously studied inferential task (sports
understanding). We show that CFT outperforms end-to-end learning even with
equal amounts of data, and gets consistently better as more component tasks are
modeled via fine-tuning. Compared with chain of thought prompting, CFT performs
at least as well using LMs only 7.4% of the size, and is moreover applicable to
task domains for which data are not available during pretraining.

本文介绍了一种基于将目标任务分解成组成任务并在这些组成任务的课程中微调较小语言模型的方法，即组合微调，应用于两个领域的推荐任务和一个推理任务，并证明其性能比端到端学习更好。

通过组合微调语言模型学习执行复杂任务

Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models

Pretrained large language models (LLMs) are widely used in many sub-fields of
natural language processing (NLP) and generally known as excellent few-shot
learners with task-specific exemplars. Notably, chain of thought (CoT)
prompting, a recent technique for eliciting complex multi-step reasoning
through step-by-step answer examples, achieved the state-of-the-art
performances in arithmetics and symbolic reasoning, difficult system-2 tasks
that do not follow the standard scaling laws for LLMs. While these successes
are often attributed to LLMs' ability for few-shot learning, we show that LLMs
are decent zero-shot reasoners by simply adding "Let's think step by step"
before each answer. Experimental results demonstrate that our Zero-shot-CoT,
using the same single prompt template, significantly outperforms zero-shot LLM
performances on diverse benchmark reasoning tasks including arithmetics
(MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin
Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled
Objects), without any hand-crafted few-shot examples, e.g. increasing the
accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with
large InstructGPT model (text-davinci-002), as well as similar magnitudes of
improvements with another off-the-shelf large model, 540B parameter PaLM. The
versatility of this single prompt across very diverse reasoning tasks hints at
untapped and understudied fundamental zero-shot capabilities of LLMs,
suggesting high-level, multi-task broad cognitive capabilities may be extracted
by simple prompting. We hope our work not only serves as the minimal strongest
zero-shot baseline for the challenging reasoning benchmarks, but also
highlights the importance of carefully exploring and analyzing the enormous
zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or
few-shot exemplars.

该研究发现预训练的大型语言模型可以成为良好的零样本推理器，提出了一种零样本的 CoT 方法，将其用于多种复杂的推理任务并取得了显著的性能提升。