GPT-4 Takes the Lead in Large Language Model Instruction Fitting: Advanced Generalization Capabilities for Real-World Tasks

The excellent generalization abilities of extended language models (LLMs), such as learning in context and thought chain reasoning, have been demonstrated. Researchers have been looking at instruction-setting techniques for LLMs to help them follow plain-language instructions and complete real-world jobs. This is accomplished by supervised fine-tuning using manually enhanced publicly available benchmarks and data sets, automatically created instructions, or by training the model in various tasks using human-annotated prompts and feedback.

The instruction fitting field of study has developed efficient ways to increase the zero-and-few-trial generalization capabilities of LLMs. Self-instruction tuning, one such technique, aligns LLMs with human purpose by learning from instruction tracking data produced by leading-edge LLM instructors who have fine-tuned their instructions. With the fine-tuning of instructions, the recent success of ChatGPT and GPT-4 provides a wealth of opportunities to improve open source LLMs. A group of open source LLMs called LLaMA performs on par with commercial LLMs like GPT-3.

With its high performance and low cost, the Self-Instruct setting has been easily adapted to train LLaMA to obey instructions. For example, Vicuna uses about 700,000 instruction trace samples shared by user-ChatGPT, while Stanford Alpaca uses 52,000 instruction trace samples produced by GPT-3.5. Initially, they suggest using GPT-4 as a master for self-instruction tuning to improve next-generation instruction tuning for LLMs.

In this study, Microsoft researchers contribute the following:

• GPT-4 data: They make available data produced by GPT-4, such as the 52K English and Chinese instruction tracking dataset, and feedback data produced by GPT-4 that qualify the results of three instruction-fit models.

• Models and evaluation: They have created reward models and instruction-fit LLaMA models using the data collected by the GPT-4. They employ three metrics evaluated on test samples (ie, invisible instructions) to measure the effectiveness of instruction-adjusted LLMs: human evaluation on three alignment criteria, automated evaluation using GPT-4 feedback, and ROUGE-L on artificial instructions.

In this research, the efficiency of instruction tuning using GPT-4 is demonstrated. His empirical research confirms the value of using the data provided by GPT-4 to tune LLM instructions. Provides helpful tips for creating a general-purpose, LLM-based instruction-following agent. They release 52K English and Chinese instruction trace instances built with GPT-4 along with LLaMA tuned model checkpoints in the hope that their empirical findings and resources will help create open source and mainstream LLMs that may perform better. human values ​​to complete the tasks.

This is still a work in progress, and many avenues can be investigated: Scale of the data and model. The base size of the LLaMA model is 7B, while the GPT-4 data size is 52K. Vicuna uses the 13B LLaMA model and gathers around 700,000 conversion shifts (based on ShareGPT multi-shift data). It would be encouraging to continue collecting additional GPT-4 instruction tracing data, integrate it with ShareGPT data, and train larger LLaMA models to increase performance. RLHF is (ii). The use of the reward model during the decoding phase means that the comparative data is likely to offer relevant feedback for LLM training. It seems sensible to continue putting LLMs through reward model training, such as reinforcement learning with machine-generated feedback. They make the data generated with GPT-4 and the codebase public.

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