Summary

This video introduces two different expectations about large language models: Fine-tuning and Prompting, corresponding to different techniques. Fine-tuning uses training data to fine-tune the model for a specific task; Prompting provides the model with instructions described in human language, with the expectation that the model will correctly understand the instructions and generate the correct answers. Both approaches have advantages and disadvantages.

In this part, we discuss further about instruction learning and in-context learning in natural language processing. We explore how machines can be taught to understand human instructions and learn from examples to perform various NLP tasks. The FLAN model is used as an example of instruction tuning, where various NLP tasks are rewritten as instructions and used to train the model. The results show that large language models can learn to understand human language instructions and perform NLP tasks accurately.

In the final part, we introduce various techniques to improve the performance of language models, including chain of thought, self-consistency, confidence scores, majority voting, soft prompt and reinforcement learning. Among them, zero-shot CoT and pre-trained language model techniques for automatic instruction generation are relatively novel approaches. The article also provides a related Tutorial for readers’ reference.

Fix grammar and spelling: This video introduces two different expectations for large language models: Fine-tuning and Prompting, which correspond to different techniques. Fine-tuning uses training data to fine-tune the model for a specific task, while Prompting provides the model with instructions described in human language, with the expectation that the model will correctly understand the instructions and generate the correct answers. Both approaches have advantages and disadvantages.

In this section, we discuss instruction learning and in-context learning in natural language processing. We explore how machines can be taught to understand human instructions and learn from examples to perform various NLP tasks. The FLAN model is used as an example of instruction tuning, where various NLP tasks are rewritten as instructions and used to train the model. The results show that large language models can learn to understand human language instructions and perform NLP tasks accurately.

In the final part, we introduce various techniques to improve the performance of language models, including chain of thought, self-consistency, confidence scores, majority voting, soft prompt, and reinforcement learning. Among them, zero-shot CoT and pre-trained language model techniques for automatic instruction generation are relatively novel approaches. The article also provides a related tutorial for readers’ reference.

Outlines

  1. Fine-tuning and prompting are two different techniques for adapting a language model to meet different expectations.
  2. Fine-tuning involves using training data to adjust the model for a specific task, while prompting provides the model with instructions in human language, with the expectation that it will understand and generate accurate responses.
  3. Fine-tuning requires pairs of data, whereas prompting is more flexible but requires different instructions for different tasks.
  4. To improve the efficiency of fine-tuning, the adapter technique can be used to adapt the model to multiple tasks simultaneously.
  5. Prompting is a feature that guides large language models to generate desired content, while fine-tuning makes models more adaptable to new data, often using domain adaptation.
  6. Although training models can be time-consuming, fine-tuning and prompting can save time and improve accuracy.
  7. Prompting enables people to provide more precise guidance, creating high-quality generative content such as articles, scripts, and dialogues.
  8. Fine-tuning is considered an advanced technique in NLP, with GPT-3’s fine-tuning feature allowing users to reduce input significantly.
  9. The prompting approach allows people to challenge human language skills by asking for almost impossible tasks, such as speaking English in the style of Shakespeare.
  10. Finally, both finetuning and prompting are widely used in different domains, but the choice of which one to use needs to be considered in the context of the specific application.
  11. This section introduces various techniques to improve the performance of language models, including Chain of Thought, Self-consistency, Confidence Scores, Majority Voting, Soft Prompt, and Reinforcement Learning.
  12. Zero-shot CoT and pretend language model techniques for automatic instruction generation are relatively new approaches.
  13. The Confidence Scores method is not helpful in practice, and it is better to use the majority form directly.
  14. The CoT method may not be helpful for some powerful language models, such as ChatGPT.
  15. List to Most Compute is a way to break down difficult mathematical problems into simpler mathematical problems.
  16. By letting the machine make its own inferences, the diversity of the answer can be improved, thus enhancing model performance.
  17. By combining simple and complex problems, the machine can better understand the problem and provide the correct answer.

Highlights

  • [06:58] Humans have always had advantages and disadvantages for being generalists and specialists.
  • [07:10] A specialist will focus on one task and outperform other types of AI on that task, e.g. ChatGPT is better at translation tasks than a single task.
  • [07:50] If you use a Prompting approach to handle translation with ChatGPT, you can first try multiple prompts and then synthesize and analyze the best one to achieve higher performance.
  • [09:56] Microsoft’s research shows that ChatGPT does not perform as well as the commercial system and the strongest translation model, WMT, when used as translation software, showing that software that focuses only on translation performs better.
  • [11:20] The advantage of being a generalist is that if you need to develop different features in the future, you can directly use the versatility of ChatGPT and manipulate the model in natural language without having to write code.
  • [12:29] When using a BERT model, it is often necessary to modify it and add plug-ins and fine-tune parameters to make it a specialist for various tasks.
  • [13:13] The reason for adding plug-ins to the BERT model is that it does not have the ability to generate complete sentences, it only fills in the spaces and requires plug-ins to generate complete sentences.
  • [13:31] If you need BERT to handle translations or generate answers in a particular format, you can’t use BERT directly and need to develop or add a plug-in to implement such functionality.
  • [13:46] When using a generalist model such as ChatGPT for multiple tasks, it is also necessary to design Prompt for different tasks to achieve better performance.
  • [11:11] The benefits of being a generalist are that it fits into the human imagination of artificial intelligence, it’s trendy, it’s worth discussing, and it allows for rapid development of new features in the future.
  • [13:55] Introduce how to add plug-ins to BERT, need to make adjustments and attach additional modules to be able to accomplish the task.
  • [14:30] Fine-tuning (finetune) is the optimization of an existing language model trained with paired data to make it a translation specialist, which requires gradient descent.
  • [15:30] Fine-tuning (finetune) is tuning the parameters of a neural-like network to train a language model, which actually means running gradient descent.
  • [16:21] Adapter can be inserted inside a large language model, and can be used to add new tasks to the language model without moving the original language model.
  • [18:01] Adapter can be inserted in a variety of ways, such as Bitfit, Houlsby, and Prefix, to achieve improved performance, but the choice of insertion location is determined by the task.
  • [19:56] Language models are very large, such as GPT-3, which has too many references. If a model needs to solve hundreds of tasks, then it needs to store many models, which is unaffordable in practice.
  • [20:13] The adapter approach is thus used. Adapters are additional modules that are inserted into existing models to perform additional tasks without changing the entire model.
  • [20:27] Large language models only need to store one, it has 17.6 billion parameters, but only adapters need to be stored per task.
  • [20:38] Adapter is a small parameter, so the more tasks there are, the more adapters are needed.
  • [20:50] Adapter is easy to use because it has few parameters, which is why people use adapter technology.
  • [21:08] Expectation 1 is the way people usually use BERT models, and the next step is to explain expectation 2: turning models into generalists.
  • [21:36] Adapter can replace fine-tune for the whole large language model, and only fine-tune adapter is needed to improve performance.
  • [21:59] Once the performance is improved, efficient fine-tuning can be done to turn the neural network into a generalist.
  • [22:11] Prompt is a way to help humans use the GPT-3 model easily, by giving some keywords, GPT-3 can continue to complete the article.
  • [22:27] The problem with Prompt is that its correctness sometimes causes bias, so it requires constant adjustment of the prompt.
  • [22:42] Another problem with Prompt is that it can be a risk to enterprise content and therefore requires special preparation and management.
  • [23:00] So, while Prompt is a convenient tool, it needs to be used carefully to avoid any risks and misunderstandings.
  • [13:14] Finetuning and Prompting are different ways of using large language models, Finetuning is suitable for similar tasks and Prompting is for machines to learn themselves for specified task requirements.
  • [14:11] Large language models have the ability to learn from the wrong data and learn the right content from the bad content.
  • [15:27] Another way of using natural language processing models to learn another task is through Prompting, which allows the machine to complete the requirements of the corresponding task based on sentence instructions.
  • [16:14] The process of Prompting to allow the machine to understand the task based on different instructions and give the appropriate response is called Instruction Tuning.
  • [17:41] Getting the machine to do the task expected by humans through instructions requires instruction tuning fine tuning to achieve perfect results.
  • [18:37] Instruction tuning allows the model to be fine-tuned by giving the model different instructions to guide the model to understand the human task requirements and provide the correct answer.
  • [19:16] T0 is a well-known model for instruction tuning, which teaches the model the task requirements through instructions so that the model can make correct judgments about the task.
  • [19:26] Generative AI for large speech models already has the ability to learn from incorrect data and can learn from bad content to correct content, and this can be achieved through Prompting.
  • [19:35] The need to enable machines to complete corresponding tasks based on sentence instructions through Prompting is another way that natural language processing models are used for this task.
  • [19:37] Getting the machine to complete the task expected by humans through instructions requires instruction tuning fine tuning to achieve perfection.
  • [19:43] Finetuning and Prompting are two different ways to use generative AI models, Finetuning expects the model to perform well on tasks such as natural language inference, while Prompting helps the model perform a specific task through instructions.
  • [19:54] The model is required to be able to perform tasks such as natural language inference at test time, but the corresponding dataset is not necessarily needed for instruction training, which requires training with Prompting.
  • [20:03] FLAN is an instruction training model that trains a model by collecting natural language processing tasks and labeled datasets and rewriting the tasks into instructions.
  • [20:21] The instruction training step typically involves collecting various natural language processing tasks and labeled datasets, rewriting the tasks into instructions and training them with the model.
  • [20:32] Training instructions is the focus because machines that talk to humans need to be able to understand natural language task instructions, and FLAN collects various natural language processing tasks and their various instructions.
  • [20:42] Translating natural language processing tasks into instructions for training is important for training human conversable machines. FLAN collects various natural language processing tasks and the corresponding instructions for training.
  • [21:15] The instructions used in training and testing can take many forms, and FLAN uses ten different descriptions to describe the instructions for various natural language processing tasks.
  • [21:45] Instructing tasks allows generative AI models to understand human task requirements, and FLAN uses human-understandable language to collect tasks and train models.
  • [22:49] Models trained using instructions can respond intelligently through human task demands. FLAN’s instruction training outperforms GPT-3’s few-shot and zero-shot, demonstrating that the effect of instruction training is indeed significant.
  • [24:12] This result shows that the instruction training model is indeed helpful for the performance improvement of generative AI models, and the machine can achieve intelligent responses based on human instructions and can generalize to instructions that are not seen
  1. Fine-tuning vs.Prompting (1/3): https://www.youtube.com/watch?v=F58vJcGgjt0
  2. Fine-tuning vs.Prompting (2/3): https://www.youtube.com/watch?v=aZ_jXZvxyVg
  3. Fine-tuning vs.Prompting (3/3): https://www.youtube.com/watch?v=HnzDaEiN_eg