背景
看了新一期的阮一峰周刊,引用了一篇博客 Information Extraction
with Large Language Models - Parsing Unstructured Data with
GPT-3
Information
Extraction with Large Language Models - Parsing Unstructured Data with
GPT-3 (marcotm.com)
In the past months, ChatGPT has been dominating the news headlines,
and people are both excited and scared by its quite sophisticated
ability to generate texts. Besides short- and long-form text generation,
there are quite a few other use cases which provide a lot of practical
value. With the current generation of these large language models
(LLMs), many of the classic tasks in Natural Language Processing (NLP)
such as text classification, sentiment analysis, or named entity
recognition, are almost trivial to solve.
在过去的几个月里,ChatGPT
一直占据着新闻头条,人们对它相当复杂的文本生成能力既兴奋又害怕,除了生成短格式和长格式文本外,还有许多其他用例提供了很大的实用价值
随着这些大型语言模型(LLM)的出现,自然语言处理(NLP)中的许多经典任务,如文本分类、情感分析或命名实体识别,几乎都很难解决
In this article, I have documented some experimentation with how to
use GPT-3 (update: and 3.5) to extract structured information
from unstructured texts and I hope the article can serve as a tutorial
for how to approach such a task with an LLM.
在这篇文章中,我记录了一些关于如何使用 GPT-3(更新:和
3.5)从非结构化文本中提取结构化信息的实验,我希望这篇文章可以作为如何使用
LLM 处理此类任务的教程
作者维护了一个招聘网站,但是招聘信息是以非结构化文本形式进行投递,作者希望将其重要信息提取出来,维护数据后用户可以通过相关性进行查询