Alibaba Researchers Introduce AUTOIF: A New Scalable and Reliable AI Method for Automatically Generating Verifiable Instruction Following Training Data
What challenges do LLMs face in following complex instructions?
Large language models (LLMs) struggle with following complex instructions due to the difficulty in automatically generating high-quality training data without manual annotation. Traditional methods involving human annotators are time-consuming and hard to scale, while behavior imitation can lead to unreliable training data as advanced models like GPT-4 can make mistakes2. These challenges hinder LLMs' ability to execute complex tasks correctly, especially in critical scenarios.
What are Large Language Models (LLMs) designed to do?
Large Language Models (LLMs) are designed to understand, interpret, and generate human language. These models are trained on massive datasets and can perform tasks such as translation, summarization, and producing conversational responses. They aim to accurately and reliably follow complex instructions, which is crucial for practical applications like customer service bots and advanced AI assistants.
How does AUTOIF ensure the reliability of generated training data?
AUTOIF ensures the reliability of generated training data by transforming the validation process into code verification. It requires LLMs to create instructions, corresponding code to check response correctness, and unit test samples to verify the code. This approach leverages execution feedback-based rejection sampling to generate data suitable for Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). Only high-quality data that passes the verification code is used for training, significantly improving the instruction-following capabilities of LLMs4.