Why are novices more interested in doing vertical field SFT fine-tuning than experts?

As the wave of artificial intelligence sweeps the world, the application of large language models (LLMs) is becoming ubiquitous. SFT fine-tuning (Supervised Fine-Tuning) in vertical fields has attracted countless attention as a key technology to improve the professional capabilities of models. But have you noticed a strange phenomenon: compared with experienced experts, novices seem to show greater enthusiasm for trying SFT fine-tuning? Why is this? Is it the fearless adventurous spirit of novices, or the reserved attitude of experts after careful consideration? So, what is SFT fine-tuning? Why is it so important? How to do it specifically? Which model should I start with? Don't worry, this article will answer you step by step.

1. Why is it necessary to fine-tune SFT in vertical fields?

Simply put, SFT fine-tuning is to use vertical field data to "tune" the large language model again on the basis of the large language model, so that it can be more knowledgeable and professional. Why do we have to do this? There are mainly the following reasons:

1. Make up for the shortcomings in domain knowledge

The general large language model is trained on a large amount of Internet data. Although it has a wide range of knowledge, it lacks depth in certain professional fields. For example, if a doctor asks the model about the diagnostic criteria for a certain disease, or a lawyer wants to know the details of a certain regulation, the general model may be "confused" and give an inaccurate or even completely wrong answer. Through SFT fine-tuning, we can use professional data in the field (such as medical literature and legal provisions) to train the model, allowing it to quickly master professional terms and knowledge and be more confident when answering questions.

2. Learn to abide by industry rules

Every industry has its own "rules of the game". For example, the medical field needs to strictly protect patient privacy, and the legal field must follow procedural justice. These norms require extremely high professionalism. General models may not know these "unspoken rules" and may even make some low-level mistakes. SFT fine-tuning can "teach" these norms to the model through training data, allowing it to learn how to operate in compliance in vertical scenarios.

3. Adapt to different task requirements

Tasks in different fields vary greatly. Doctors may need models to help write medical records, lawyers may need to review contracts, and financial analysts may want to predict stock trends. General models often seem "unsuitable" for these specific tasks. Through SFT fine-tuning, we can use real input and output samples (such as "question-answer" pairs) to train the model, so that it can become familiar with the process and requirements of the task and truly "do one thing like a professional".

4. Add the icing on the cake and enhance the experience

Even if the general model can already handle some domain tasks, SFT fine-tuning can take it to the next level. The text generated by the fine-tuned model will be smoother and more in line with industry practices, making it easier and more comfortable for users to use.

In general, SFT fine-tuning is like a set of "professional skills training" tailored for the large language model, turning it from a "jack of all trades" into an "expert" in a certain field.

2. How to fine-tune SFT in vertical fields?

SFT fine-tuning is not just throwing some data at the model, it has a clear process. Let's break it down step by step:

1. Prepare domain data

• Collect data : First, you need to find high-quality domain texts, such as medical reports, legal documents, financial news, etc. These data can be structured tables or ordinary articles.

• Clean data : remove noise (spelling errors, useless information) and sensitive content (such as personal information) from the data to ensure that the data is clean and usable.

• Label data : According to the task requirements, organize the data into a vertical format, such as "question-answer" pairs, "original text-summary" pairs, etc., to facilitate model learning.

2. Construct fine-tuning samples

• Design format : Design sample templates for input and output according to the model characteristics and task requirements. For example, for a medical consultation task, the input may be "patient symptoms" and the output may be "diagnosis suggestions".

• Enrich content : You can use domain knowledge graphs or industry specification documents to add more background information and constraints to the input samples.

• Organize the data set : Organize these samples into a data set for subsequent training.

3. Design fine-tuning strategy

• Model selection : Find a general model that is close to the task as a starting point, so that fine-tuning will be more efficient.

• Set goals : In addition to teaching the model to generate language, some task-related assessment indicators should also be added, such as accuracy, compliance, etc.

• Adjust parameters : Set parameters such as learning rate and number of training rounds to find the best balance between performance and cost.

4. Training the Model

• Start training : Use the prepared dataset to train the selected model.

• Dynamic adjustment : Observe the results while training, adjust parameters according to the situation, and ensure that the model gradually improves.

• Select the best version : Multiple versions of the model will be generated during the training process, and the one with the best performance will be selected after testing.

5. Evaluate the model

• Thorough testing : Check the model with independent test data, both for language quality and task completion.

• Expert review : Ask domain experts to look at the model’s output, give their opinions, and identify deficiencies.

• Repeated optimization : Based on the test results, adjust the data or model and make multiple rounds of improvements.

6. Deploy the application

• Online use : Deploy the optimized model to the actual scenario to provide intelligent support.

• User interface : Design an easy-to-use interface so that users can get started easily.

• Continuous improvement : Collect user feedback and update the model regularly to keep up with changes in domain knowledge.

Through these six steps, an intelligent assistant for a specific field is born!

3. Which model should I start fine-tuning? Pedestal model vs. conversational model

When fine-tuning SFT, a key question is: where to start? Should I choose a pre-trained base model (also called a base model) or a conversational model (such as a chat model)? Both options have their pros and cons, so let’s analyze them one by one.

1. Fine-tune on the pre-trained base model

advantage:

• Solid language skills : The base model has been trained on large-scale data and has strong language understanding and generation capabilities, which is a good foundation.

• High flexibility : It is not fixed to a certain task and can be freely adjusted according to field requirements during fine-tuning.

• Lower cost : Fine-tuning the base model requires less computing resources and time than fine-tuning the conversational model.

shortcoming:

• Domain knowledge gaps : The base model has almost no understanding of the professional field and needs more data to "make up for it."

• Weak dialogue capabilities : It is good at handling single paragraphs, but may not be coherent enough in dialogue scenes.

2. Fine-tune the dialogue model

advantage:

• Good conversation foundation : The conversation model has learned the basic routines of chatting and can adapt to domain conversation tasks more quickly.

• Strong coherence : It can better understand the context and keep the logic and naturalness of the conversation.

• Good user experience : The fine-tuned model can provide an interaction method that is closer to human communication.

shortcoming:

• Possibly biased : The conversation model may have some common conversation habits that may not be suitable for vertical fields.

• Insufficient knowledge depth : It is more inclined towards conversational scenarios and may not have sufficient grasp of deep domain knowledge.

• Higher cost : Conversational models are usually more complex and require more resources to fine-tune.

3. How to choose?

• If you want a professional dialogue system and have sufficient data and computing power, then start with the dialogue model. It can quickly adapt to the characteristics of the field and provide a better user experience.

• If your data is limited or your task is more general , it may be more cost-effective to start with a base model. You can build a decent model with fewer resources.

• Flexible combination : You can also use the base model to lay the foundation for the domain first, and then use dialogue optimization to improve the experience; or try "prompt engineering" to quickly adapt to the domain by designing input templates.

In short, which path you choose depends on your specific needs: what is the task? How much data do you have? How big is the budget? Only after weighing them clearly can you find the most suitable solution.

4. What are the practical applications of SFT fine-tuning?

SFT is only performed when the model capability is insufficient and RAG cannot be used. The application scenarios of SFT fine-tuning are very wide and almost every industry can benefit from it. Here are a few typical examples:

1. Healthcare

• Medical conversational assistant : Patients input symptoms, and the model gives preliminary diagnostic suggestions or health guidance.

• Medical report generation : Automatically generate medical records or imaging reports based on examination results.

• Drug research and development assistance : analyze molecular structure, predict drug effects, and assist in the development of new drugs.

2. Legal and judicial fields

• Legal consultation assistant : answer legal questions for ordinary people, popularize knowledge, and provide litigation advice.

• Contract Review Assistant : Scan contract terms, identify potential risks and propose amendments.

• Judgment document analysis : extract key information from massive documents to support case studies.

3. Financial and economic fields

• Financial information summary : extract key points from news and generate brief news or market comments.

• Stock trend forecast : Combine financial reports and market data to predict stock trends.

• Economic report writing : Automatically generate industry analysis or macroeconomic reports.

These examples are just the tip of the iceberg, the potential for SFT fine-tuning goes far beyond this.

V. Conclusion

SFT fine-tuning in vertical fields is the key to unlocking the potential of large language models. Through it, we can turn general "all-round players" into "top experts" in a certain industry. Whether it is medical diagnosis, legal consultation, financial analysis, or educational counseling, as long as the right method is used, SFT fine-tuning can make the model shine. In general, novices' enthusiasm for SFT fine-tuning in vertical fields stems from their curiosity about technology, desire for results, and relative indifference to risks, while experts are more cautious due to their rich experience and clear understanding of the boundaries of technology. Whether you are a novice entering the field of AI or an expert who has been working in the field for many years, it is crucial to understand the value and limitations of SFT fine-tuning. May this article ignite your thinking spark and inspire you to find your own answers in the exploration of AI in vertical fields.