Choosing the right SFT dataset for your LLM
🧩 SFT Dataset: Top essential datasets to boost your LLM
Large Language Models (LLM) such as GPT-4, LLama, or Mistral have revolutionized natural language processing by making interactions with AI more fluid and relevant. However, to achieve optimal performance on specific tasks, these models require a supervised curing, or Supervised Fine Tuning (SFT). Supervised fine tuning is the common approach for adapting large language models to specific tasks. This technique makes it possible to fine tune an existing pre-trained model to specific needs, by exposing it to a set of annotated and structured data in the right format.
The choice of a SFT dataset is therefore a decisive step in training a successful specialized model. A good dataset directly influences the ability of the model to understand, generate, and interact in a more natural and accurate way. When preparing datasets for fine tuning, it is important to provide clear details and documentation, including dataset expectations and step-by-step processes for data collection, prompt generation, verification, and evaluation. Documentation from clients or domain experts is often used as a source for dataset creation, helping to extract relevant context, keywords, and categories. Various processes are involved in curating and preparing SFT datasets to ensure quality and consistency. Some datasets focus on human dialogues, others on specific fields such as medicine or law, and some on multi-language or the ethics of AI. SFT datasets enable tailored solutions for different domains and tasks.
What is a Supervised Fine-tuning Trainer Dataset?
Supervised Fine Tuning (SFT), or supervised model alignment, is a technique used in machine learning to adapt a pre-trained model to specific tasks using annotated data. In SFT, a dataset class is often defined to structure and manage datasets for supervised fine-tuning, ensuring efficient integration into the AI training pipeline. Input data, which serves as the foundation for fine-tuning, consists of labeled examples—each example, or instance, in the dataset provides guidance for the model's learning process. This approach makes it possible to adjust the parameters of the model in order to improve its performance on targeted tasks, based on concrete instances provided by the dataset. During SFT, specific parameter settings are used, and model parameters are updated to optimize the model's accuracy and capabilities for the desired application. Fine-tuning enhances model capabilities for specific tasks, and model training involves multiple steps, including data preparation and parameter adjustment, to maximize the model's performance.
Difference between SFT and other methods of adapting models:
Pre-workout
Before diving into the supervised fine tuning (SFT) process, it’s important to lay a solid foundation with high quality datasets (used in pre-training). The success of your model’s performance hinges on the quality and relevance of the SFT data you use. High quality SFT datasets serve as the backbone for adapting pre-trained models to specific tasks, enabling them to generate accurate and contextually appropriate responses. To achieve this, focus on unlabelled data or data generation methods that ensure diversity, accuracy, and representativeness. Involving vetted subject matter experts in the creation and review of SFT datasets is essential—they help guarantee that the data reflects real-world scenarios and meets the standards required for your domain. By prioritizing these elements in your pre-workout phase, you set your models up for success in the fine tuning process, enhancing their ability to deliver high performance and reliable results.
Supervised Fine Tuning (SFT)
After pre-training, the model is refined using task-specific annotated data, allowing it to learn accurate input-output relationships. It is essential to maintain control over both the data and the fine-tuning process to ensure transparency, security, and effective management throughout supervised fine tuning. Implementing robust quality control measures, such as real-time monitoring and expert oversight, helps guarantee the accuracy and consistency of the fine-tuning data.
Reinforcement learning with human feedback (RLHF)
This method involves using human feedback to guide model learning, often by defining a reward function based on human preferences.
SFT Data and Generation
Generating SFT data is a pivotal step in the fine tuning workflow, as it directly shapes your model’s capabilities and output quality. Effective data generation involves creating high quality input-output pairs that mirror the specific tasks or domains your model will encounter. Techniques such as leveraging human feedback, applying data augmentation, and utilizing transfer learning can significantly enhance the quality and diversity of your SFT datasets. Human feedback, in particular, ensures that the data aligns with real user expectations and nuanced requirements. By combining these techniques, you can create high quality SFT datasets that not only improve model performance but also expand the model’s ability to handle complex, domain-specific tasks. The result is a model that delivers more accurate, relevant, and reliable responses across a variety of use cases.
What criteria define a good SFT dataset?
Data diversity
Include a variety of examples covering different use cases and ensure representation from diverse domains, such as linguistics, science, and technology, to ensure comprehensive model training and complete coverage of the task.
Annotation quality
Data should be accurately annotated to provide clear and consistent examples for the model.
Representativeness of use cases
The dataset should accurately reflect the real situations in which the model will be deployed, thus ensuring its relevance and effectiveness.
SFT Dataset Configuration
Configuring your SFT datasets is a critical step that determines how effectively your model learns from the data. The dataset should be carefully structured to include a wide range of examples that cover the full spectrum of scenarios relevant to your domain. It’s important to strike a balance between dataset size and computational efficiency—enough data to ensure comprehensive coverage, but not so much that it becomes unwieldy to process. Clear, concise instructions for human annotators are essential to maintain annotation quality and consistency. By thoughtfully organizing and configuring your SFT datasets, you ensure that your models are trained on data that is both high quality and directly aligned with the specific tasks they need to perform.
Configuring SFT Datasets for Your LLM
When configuring SFT datasets for your large language model (LLM), it’s vital to tailor the data to your model’s specific requirements and intended use cases. Start by understanding your model’s architecture, training objectives, and the evaluation metrics you’ll use to assess performance. This allows you to select and structure datasets that are both relevant and accurate for your domain. Consider factors such as dataset size, complexity, and the inclusion of domain-specific examples. Techniques like data augmentation and transfer learning can further enhance the overall quality of your SFT datasets, enabling your model to generalize better and perform more effectively. By aligning your dataset configuration with your model’s needs, you maximize the impact of supervised fine tuning and ensure high quality, reliable outputs.
SFT Dataset Size and Complexity: How Much Data Do You Need?
Determining the right size and complexity for your SFT dataset is key to achieving optimal model performance. The ideal dataset size depends on your specific requirements, the complexity of the task, and the capabilities of your model. For smaller, well-defined tasks, a dataset with 1,000 to 5,000 high quality examples may suffice. For more complex or large-scale domains, you may need 10,000 to 50,000 examples or more to capture the necessary diversity and nuance. Keep in mind that larger datasets generally lead to better accuracy and performance, but they also demand more computational resources and annotation effort. Carefully assess your domain, task complexity, and desired performance level to determine the optimal dataset size—ensuring your model is equipped to deliver accurate, high quality results tailored to your use case.
Why are SFT Datasets essential for LLMs (Large Language Models)?
Supervised Fine Tuning (SFT) datasets play a major role in the adaptation of Large Language Models (LLM) to specific tasks. SFT datasets help address the limitations and challenges faced by LLMs in handling complex or context-rich tasks by providing targeted training data. Leveraging curated data streams accelerates the fine-tuning process, ensuring high-quality and efficient model development. The use of domain specific datasets is important for improving model reliability and accuracy in particular fields or industries. Although LLMs are initially trained on large generalist data sets, the SFT allows them to be specialized for particular areas or applications.
Improving performance on specific tasks
The SFT refines the capabilities of LLMs by exposing them to annotated data that is relevant to a given task. For example, in the field of code generation, the SFT has demonstrated its effectiveness by improving the accuracy, efficiency, and readability of the code produced by models, while reducing errors and increasing security. Using well-prepared SFT datasets can also increase the speed of model adaptation and deployment, enabling faster project turnaround times.
Correcting biases and aligning model behavior
High-quality SFT datasets, developed with the expertise of professionals in the field, make it possible to create realistic scenarios that provide the context needed to train LLMs to respond appropriately. This approach helps to reduce bias and adjust model behavior to be more in line with human expectations.
Adapting LLMs to specialized fields
In sectors such as healthcare, law, or finance, LLMs must provide information that is accurate and in line with industry standards. The SFT, using specific datasets, allows models to provide relevant and accurate information, thus meeting the high requirements of these fields.
Our selection of the best SFT Datasets
In this section, we present a selection of supervised fine-tuning (SFT) datasets recognized for their quality and relevance in improving large language models (LLM). Advanced tools are often used to curate, process, and evaluate these SFT datasets, ensuring optimal model performance. Each dataset is accompanied by a description, its main characteristics and its use case.
Some examples of general datasets for Fine Tuning
OpenAssistant Conversations
This dataset is rich in human dialogues and interactions, designed to refine the conversational abilities of linguistic models. It is especially useful for applications that require a thorough understanding of human conversations.
Alpaca Dataset (Stanford)
Based on the OpenAI approach, this dataset offers a set of instruction data allowing efficient fine-tuning of models. It is widely used for the rapid establishment of efficient models in various linguistic tasks.
Dolly 2.0 Dataset (Databricks)
This open source dataset offers resources for refining open-source LLMs, making it easy to customize models for specific applications.
Multi-domain datasets
Multi-Domain SFT Dataset (Toloka AI)
Composed of 10,000 quick-response pairs, this dataset covers several languages and sectors, offering essential diversity for training models capable of managing varied contexts.
The Stack (BigCode)
Intended for the fine-tuning of LLMs specializing in computer code, this dataset provides a vast collection of source codes from various programming languages, thus improving the capabilities of models in understanding and generating code.
PubMedQA
Designed for specialized models in biomedical and medical research, this dataset contains questions and answers from the scientific literature, helping models provide accurate answers in the medical field.
Multilingual datasets
XGLUE
This multilingual benchmark is designed for the assessment and training of LLMs, offering data in various languages to improve the multilingual capabilities of the models.
M2M-100 (Facebook AI)
This translation corpus covers 100 languages, offering a valuable resource for training models that can translate directly between numerous language pairs without using a pivotal language.
Datasets for alignment with human preferences
HH-RLHF (Anthropic)
Used to align models with more secure and ethical responses, this dataset contains annotated examples to guide models toward behaviors that are in line with human expectations. Red teaming services are often employed to test and improve the robustness and security of models trained with these datasets.
InstructGPT (OpenAI)
Based on InstructGPT models, this dataset allows supervised fine-tuning on conversational tasks, improving the ability of models to follow human instructions accurately.
💡 These datasets represent essential resources for the supervised fine-tuning of LLMs, allowing them to improve their performance in various tasks and areas. But keep in mind they are public, and unlikely to deliver better performance than existing finetuned models from Anthropic or OpenAI. The key is in the data! You need better data!
How to choose the right SFT dataset for your model?
The choice of an SFT dataset depends on several essential criteria that directly influence the quality of fine-tuning and the final performance of the model. Selecting the right dataset adds significant value by enhancing the model's performance and applicability in your specific domain. Here are the main things to consider before selecting a dataset that fits your use case.
Define the specific needs of the model
Each language model has a specific purpose:
- A conversational chatbot will require a dataset rich in human dialogues and interactions (e.g. OpenAssistant Conversations).
- A model intended for the medical field should be trained on databases validated by experts (e.g. PubMedQA).
- An AI specialized in translation or transcription should rely on high-quality multilingual datasets (e.g. Flores-200).
💡 Before choosing a dataset, it is therefore essential to identify specific tasks of the model and the skills it must "develop".
Verify data quality and size
A good dataset should be:
✔ Rich and diverse: it should cover a wide range of use cases.
✔ Well annotated: Data should be accurate and free of annotation errors.
✔ Sufficient in size: the larger a dataset, the more efficient the fine-tuning is, but this must be balanced with the processing capacities and resources available.
Large datasets like the Stack (BigCode) or M2M-100 are ideal for tasks that require broad coverage and models that can generalize across a large number of cases. But they remain generic: it's always better to train / fine-tune your model using your own coding corpus.
Consider ethical constraints and data set biases
Using an SFT dataset involves ensuring that it is free of biases that could negatively influence model decisions.
- Some datasets are optimized to minimize bias and improve ethical alignment LLMs (e.g. HH-RLHF from Anthropic).
- It is best to choose transparent sources, where the origin of the data is clearly documented.
Regular evaluation of the model after fine-tuning also makes it possible to detect possible biases and to correct them.
Exploring open-source vs proprietary options
- Open source datasets: freely accessible, they offer great flexibility but often require careful pre-processing (e.g. Alpaca, Dolly 2.0, OpenAssistant Conversations).
- Proprietary datasets: often paid for, they are generally better annotated and optimized for specific use cases.
Conclusion
SFT Datasets are essential resources for refining and specializing large language models, allowing them to achieve optimal performance in specific tasks. Whether it's to improve the conversation, refine the understanding of a domain, or align a model with human preferences, choosing the right dataset is critical.
By combining data quality, diversity and ethics, LLMs can be trained more effectively and adapted to the real needs of users. Exploring the best available resources, whether open-source or proprietary, or requiring expertise from domain AI trainers, thus makes it possible to make the most of supervised fine-tuning and to build ever more efficient models and AI products!
