The collision between NLP and ChatGPT: the joint efforts of large models and small models

ChatGPT is really hot! As the terminator of NLP, how will ChatGPT collide with NLP? Can big models replace small ones? Where will NLP go from here? Today I recommend a good book to everyone: "Content Understanding Based on NLP"!

1. Background

With the vigorous development of artificial intelligence, the understanding technology of text content will be involved in a large number of application scenarios. Due to different scenarios, there is currently no unified template for understanding text content, and it is not easy to solve it. There are a variety of machine learning, deep learning, natural language processing and other materials on the market, but none of them are closely related to business scenarios. Even if they are connected, they are very general and do not involve very trivial details in actual business scenarios . question.

And only by solving these details, can we develop the business better. At the same time, there are few books on the market that explain algorithms and other related knowledge from the perspective of problems, but in business scenarios, more business problems are solved through simple and matching technologies. Therefore, it is very important to let everyone see how to disassemble the problem from the perspective of the problem, and then solve the business problem through matching technology.

The purpose of natural language processing is to enable computers to understand human language, but due to the high abstraction and ambiguity of language, it adds great difficulty to text understanding. The ChatGPT model has been very popular in recent years and has very potential commercial value. It can be integrated with many business scenarios to generate new experiences and even subvert the original methods . The large model is very powerful, but it faces great challenges in terms of model deployment, resource consumption, and data labeling. For example:

1. Model size: Pretrained models are often very large. Models such as GPT-3 contain hundreds of billions of parameters, so they may face storage and transmission difficulties when deployed.
2. Inference speed: Model inference speed is one of the important indicators to evaluate the performance of a machine learning model. In practical applications, the inference speed of the model often directly affects the user experience and system efficiency. Efficient model inference speed can shorten processing time, improve user satisfaction, and reduce waste of computing resources.
3. Computing resources: Pre-trained models require a lot of computing resources for inference, which can cause computing bottlenecks and performance issues when deployed.
4. Hardware compatibility: The deployment of pre-trained models needs to adapt to a variety of different hardware platforms, including CPU, GPU, ASIC, etc., so adaptation and optimization are required.
5. Version management: The pre-trained model may be continuously updated and improved, so version management and updates need to be considered when deploying.

2. Book introduction

Therefore, the combination of large and small models is still the best choice at present. The book "Content Understanding Based on NLP" aims to start from the perspective of text language understanding, and introduces in detail text feature representation, content repetition understanding, fluency recognition, content quality, user portrait, text summary and text error correction, etc. This book adopts the introduction and explanation of relevant technologies in the way of application throughout.

Specifically, this book mainly introduces related technologies and applications in natural language processing from the following aspects.

1)文本特征表示方法：本章详细介绍了文本特征的表示方法包括离散型表示方法和分布型表示方法以及词向量的评判标准。

2)内容重复理解：主要从应用的角度介绍了标题、段落和文章重复三种场景以及三种场景的具体实现。

3)内容通顺度识别：介绍了数据增强的几种方法，句子通顺度识别方法以及内容不通顺时的几种纠正方式。

4)内容质量：内容质量在很多场景中都是非常重要的，因此，从应用的角度详细介绍了知识问答质量体系的搭建方法，方便大家后续在业务实践中快速的使用起来。

5)用户画像：本章主要讲述了用户画像中标签体系的整个建设流程。

6)自动文本摘要生成：介绍了文本摘要生成中流行的两类方法，包括抽取式文本摘要和生成式文本摘要；此外还介绍了文本摘要的几种常用数据集以及文本摘要的评价方法。

7)文本纠错：介绍了文本中错误的类型、来源，文本纠错常用的方法与框架，并介绍了常用的文本纠错工具的安装以及使用方法。

可以看到本书中详细介绍了大量的使用场景，大家可以通过这些场景选择使用什么样的算法以及如何通过策略达到更好的业务效果。模型更多的是解决业务问题，而不是独立存在的。通过本书的学习可以更好的理解大模型的底层原理，因为本书中详细介绍了大模型的底层算法，通过这些知识的学习可以更容易理解大模型的构建。本书中使用了很多小模型可以快速的解决问题，这些小模型可以和大模型联动，将大模型压缩成小模型或者使用数据蒸馏技术，解决模型部署和响应速度等多方面问题。

3. Introduction of the author

The author of this article: Li Mingqi, a senior AI technical expert, is currently working at BAT as a senior algorithm engineer, and the author of "Content Understanding Based on NLP". Long-term commitment to the implementation of machine learning, deep learning, NLP and other technologies in actual business scenarios. He has rich experience in content understanding. The content quality project he led has won the best project award. He has published 2 academic papers related to artificial intelligence and applied for 5 invention patents in the field of artificial intelligence. Contributed a lot of codes on content quality, question answering system, NLP, etc. on GitHub, and wrote some articles related to algorithms, machine learning, and content understanding on CSDN, which are very popular.

4. Extended reading

"Content Understanding Based on NLP" by Li Mingqi, Gu Xue, Meng Ziyao

推荐语：BAT高级AI专家撰写，解密大模型底层技术，结合实际业务场景，6个维度讲解如何用NLP算法解决内容理解的各种难题，以应用贯穿全书，包含大量案例。

5. Talk to ChatGPT

Question 1: What do you think is the future development trend of NLP?

作为机器学习的一个方向，自然语言处理（NLP）在过去几年里得到了广泛的关注。未来，它将会继续发展，但是更加聚焦于以下几个方面：

1. 更好的语音识别和语音生成技术：语音技术将越来越重要，因为人们更多地希望使用语音来与机器交互。未来的发展趋势是更强大、更准确的语音识别和合成技术。

2. 深度学习和神经网络的应用：深度学习和神经网络技术在NLP领域中已有了很多成功的应用，未来这些技术会继续被广泛使用，并且将有更多的研究探索神经机器翻译等领域。

3. 更智能的对话系统：对话系统是语音交互最重要的应用场景之一，未来将会更多地关注如何让对话系统更加智能和自然。

4. 多语言处理：跨语言交互的应用日益增多，为了满足人们对多语言的需求，未来的发展趋势是构建更强大的多语言NLP模型。

5. 结合知识图谱的语义理解：NLP系统需要理解语言的语义，而知识图谱中的丰富知识可以帮助系统更好地理解语言，未来会更多地将知识图谱与NLP结合起来。

总之，NLP领域在未来将继续快速发展，人们期待着更优秀、更智能的NLP应用。

Question 2: Will small models be replaced by large models in the future?

未来可能会存在更大和更复杂的模型，但是小模型在某些情况下可能仍然有其优势。例如，在计算能力和资源有限的设备或环境中，较小的模型可以更有效地工作。此外，一些任务可能只需要相对简单的模型就能够获得良好的结果，而不需要投入更大的计算和时间资源来训练更大的模型。

因此，虽然未来可能会有更大和更复杂的模型涌现，但在某些情况下，小模型仍然会有其存在的必要性。

Question 3: What kind of collision will happen between NLP and ChatGPT?

NLP和ChatGPT都是自然语言处理领域的重要技术，它们可以相互碰撞，产生以下影响：

1. 提高对话机器人的能力：ChatGPT是一种生成式对话模型，可以生成与人类自然语言相似的对话内容。通过与NLP技术结合，可以实现更加智能化、自然化的对话机器人。

2. 加强对话情境的理解：NLP技术可以帮助ChatGPT更好的理解对话中的语境、意图等信息。这样可以更好地判断用户的意图，并提供更加准确的回答。

3. 提高对话质量：NLP技术可以帮助ChatGPT更加准确地理解用户的问题，并提供更加准确、周全的答案。这可以提高对话机器人的质量，也可以提高用户的满意度。

4. 优化对话生成过程：通过NLP技术提取对话中的关键信息和上下文，可以帮助ChatGPT更好地生成对话内容，并避免一些不合适的回答。

总之，NLP与ChatGPT的碰撞可以使对话机器人更加智能化、自然化，并提高对话的质量与用户的满意度。