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Large Language Models Gain Task Expertise from Human-Annotated Data and Open Knowledge

A new study published in the Journal of LaTeX Class Files shows that large language models (LLMs) can acquire domain-specific expertise with the help of a small amount of human-annotated data, coupled with a vast collection of publicly available pre-trained models and datasets.

The research, led by Yuncheng Yang and colleagues at Shanghai Jiao Tong University and Tencent YouTu Lab, addresses a major challenge in the field of artificial intelligence: how to train LLMs to become experts in solving specific tasks without relying on expensive and time-consuming manual annotation of vast amounts of data.

The authors propose a novel approach that leverages three key concepts:

  1. K-shot learning: Instead of relying on hundreds or thousands of labeled examples, the researchers demonstrate that a few human-verified examples, referred to as K-shot data, are enough to guide the selection of appropriate pre-trained models and datasets. For example, a user might provide just five examples of code snippets and their corresponding outputs for a particular programming language. These examples would be used to steer the model selection process towards models that are most likely to excel at that specific coding task.

  2. Mixture-of-experts: The researchers utilize a “mixture-of-experts” (MoE) system, where several pre-trained models are combined to achieve a higher level of performance. This approach is similar to how humans often utilize the expertise of multiple specialists to tackle complex tasks. For instance, a doctor might consult a cardiologist, a radiologist, and a surgeon to diagnose and treat a complex medical condition.

  3. Data augmentation: To further enhance the model’s expertise, the researchers propose a strategy to augment the K-shot data with relevant instructions and responses from publicly available datasets. This process helps to ensure that the model is exposed to a wider range of scenarios and examples relevant to the task at hand. A similar analogy would be a student who, after receiving a few examples from their teacher, also supplements their learning by reading relevant articles and books.

The researchers demonstrate the effectiveness of their proposed approach by evaluating their model on six different downstream tasks, including common sense reasoning, question answering, code generation, and mathematical problem solving. They find that their model consistently outperforms existing approaches, including those that rely on random model selection, traditional full-parameter fine-tuning, or simply merging pre-trained models.

This research has significant implications for the development of LLMs with specialized expertise. The ability to train LLMs effectively with just a few human-annotated examples and open knowledge has the potential to revolutionize how we approach AI development and deployment across a wide range of applications.

The authors plan to release the code and models used in their study to facilitate further research and development in this area.