RAG Fine Tuning and RAFT Explained

1. Data Quality: The quality of the data used for fine-tuning is critical. Poor quality or biased data can negatively impact the model’s performance.
2. Computational Resources: Fine-tuning requires significant computational resources, including powerful hardware and extensive training time.
3. Expertise: Fine-tuning LLMs effectively requires expertise in machine learning and domain-specific knowledge to select appropriate data and tune hyperparameters.

RAFT (Retrieval Augmented Fine-Tuning)

Figure from Microsoft Build - Resource - RAFT (Retrieval Augmented Fine-Tuning)

Retrieval Augmented Fine-Tuning (RAFT) is an advanced technique that combines the strengths of retrieval-augmented generation with the benefits of fine-tuning. RAFT involves fine-tuning an LLM using a combination of retrieved external documents and task-specific data.

1. Enhanced Accuracy: By leveraging external knowledge, RAFT improves the model’s accuracy and contextual understanding.
2. Improved Generalization: RAFT models can generalize better to new queries by incorporating diverse information from external sources during fine-tuning.

Training and Inference Procedures in RAFT

1. Training: During training, the model learns to retrieve relevant documents and use them to generate responses. This process involves optimizing both the retrieval mechanism and the generation model simultaneously.
2. Inference: At inference time, the model first retrieves relevant documents based on the input query and then generates a response using the retrieved information.

Performance of RAFT Compared to Other Models

RAFT models generally outperform standard fine-tuned models and in-context learning approaches in terms of accuracy, relevance, and contextual understanding. The integration of external knowledge sources allows RAFT models to handle a broader range of queries with higher precision and recall.

Preparation and use of Datasets for Fine-tuning

Creating Training, Validation, and Evaluation Datasets

1. Training Datasets: These datasets are used to train the model. They should be large, diverse, and representative of the tasks the model will perform. Ensuring high-quality and relevant data is crucial.
2. Validation Datasets: These datasets are used to tune hyperparameters and prevent overfitting. They help in evaluating the model’s performance during the training process.
3. Evaluation Datasets: These datasets are used to assess the final performance of the model after training. They should be distinct from the training and validation datasets to provide an unbiased evaluation of the model’s capabilities.

Proper preparation of these datasets involves data cleaning, annotation, and ensuring diversity and relevance to the target tasks. This process is vital for achieving high performance in fine-tuned models.

Summary

The advancements in language model techniques such as in-context learning (ICL), retrieval augmented generation (RAG), fine-tuning, and retrieval augmented fine-tuning (RAFT) have significantly improved the capabilities of large language models (LLMs). While each technique has its benefits and challenges, collectively, they offer a comprehensive toolkit for enhancing the accuracy, relevance, and contextual understanding of LLMs across diverse tasks and domains. The integration of external knowledge sources, fine-tuning for specific tasks, and balancing precision and recall are critical elements in advancing language processing technologies. These techniques pave the way for more sophisticated and effective language models capable of handling complex queries and generating coherent responses with greater accuracy.

Note. The above content is based on a session Practicalities of Fine-Tuning Llama 2 with AI Studio at Build 2024.