英文:
Is it possible to build a text classifier using existing LLM like chatgpt?
问题
在预 LLM 时，当我想要构建文本分类器（例如，情感分析模型，当给定输入文本时，返回“积极”，“中性”或“消极”）时，我将不得不收集大量数据，选择模型架构，并投入资源进行模型训练。
现在，由于类似ChatGPT和Google Bard这样的LLM非常智能，我想知道是否可能基于这些LLM构建相同的文本分类器。（我假设这将需要更少的数据和更少的资源。）
这是否可能？有没有教程或指南我可以参考？谢谢。
英文:
Pre LLM, when I want to build a text classifier (e.g., a sentiment analysis model, when given an input text, it returns "positive" or "neutral" or "negative"), I'll have to gather tons of data, choose a model architecture, and spend resources training the model.
Now as the LLMs like ChatGPT and Google Bard are very smart, I'm wondering if it is possible to build the same text classifier based on those LLMs. (I'm assuming this will require less data and less resources.)
Is this possible? Is there a walk through or tutorial I can follow? Thanks.
答案1
得分: 2
从openai cookbook中有：
嵌入在数据较少的情况下可能更容易，但一旦有大量数据，微调应该效果更好。
都不要使用大型LLM模型，因为ChatGPT（GPT3.5/GPT4）和Bard更适用于回答问题，而不是文本分类，所以它们不是特别有用。如果你尝试使用这些大型模型进行文本分类（使用提示），你会发现它们不一致（取决于你具体要分类的内容）。
英文:
From the open ai cookbook there is:
Embeddings can be easier with a smaller amount of data than Fine-Tuning but fine-tuning should work better once you have a lot of data.
Neither use the big LLM models as both ChatGPT (GPT3.5/GPT4) and Bard are more trained to answer questions and not as a text classifier so they are not that useful. If you do try to use these big models for text classification (using prompts) you will find them inconsistent (depending on what you specifically are trying to classify).
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论