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). This technique makes it possible to adapt a pre-trained model to specific needs, by exposing it to a set of annotated and structured data.
The choice of Dataset SFT is therefore a decisive step in training a successful model. A good dataset directly influences the ability of the model to understand, generate, and interact in a more natural and accurate way. 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.
What is a Supervised Fine-tuning Trainer Dataset?
The 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. This approach makes it possible to adjust the parameters of the model in order to improve its performance on targeted tasks, based on concrete examples provided by the dataset.
Difference between SFT and other methods of adapting models:
Pre-workout
The model is initially trained on a large set of unannotated data to learn general language representations.
Supervised Fine Tuning (SFT)
After pre-training, the model is refined using task-specific annotated data, allowing it to learn accurate input-output relationships.
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.
What criteria define a good SFT dataset?
Data diversity
Include a variety of examples covering different use cases to ensure 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.
Why are SFT Datasets essential for LLMs (Large Language Models)?
Les Supervised Fine Tuning (SFT) Datasets play a major role in the adaptation of Large Language Models (LLM) to specific tasks. 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.
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). 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.
Flores-200 (Meta AI)
This dataset is intended for fine-tuning translation models, covering 200 language pairs, and is essential for developing high-quality machine translation 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.
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.
How do you 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. 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:
- One Conversational chatbot will require a dataset rich in human dialogues and interactions (e.g. OpenAssistant Conversations).
- A model intended for medical field should be trained on databases validated by experts (e.g. PubMedQA).
- An AI specialized in translation 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 he 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.
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 (e.g. commercial datasets from OpenAI or Anthropic).
Conclusion
Les 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, thus makes it possible to make the most of supervised fine-tuning and to build ever more efficient models.
