Task Oriented In-Domain Data Augmentation
This addresses the need for better domain adaptation in LLMs for specialized fields like law and advertisement, though it is incremental as it builds on existing continual pre-training methods.
The paper tackles the problem of scarce and non-task-aware in-domain data for continual pre-training of LLMs in specialized domains by proposing TRAIT, a framework for task-oriented in-domain data augmentation, which improves LLM performance by 8% in advertisement and 7.5% in math domains.
Large Language Models (LLMs) have shown superior performance in various applications and fields. To achieve better performance on specialized domains such as law and advertisement, LLMs are often continue pre-trained on in-domain data. However, existing approaches suffer from two major issues. First, in-domain data are scarce compared with general domain-agnostic data. Second, data used for continual pre-training are not task-aware, such that they may not be helpful to downstream applications. We propose TRAIT, a task-oriented in-domain data augmentation framework. Our framework is divided into two parts: in-domain data selection and task-oriented synthetic passage generation. The data selection strategy identifies and selects a large amount of in-domain data from general corpora, and thus significantly enriches domain knowledge in the continual pre-training data. The synthetic passages contain guidance on how to use domain knowledge to answer questions about downstream tasks. By training on such passages, the model aligns with the need of downstream applications. We adapt LLMs to two domains: advertisement and math. On average, TRAIT improves LLM performance by 8% in the advertisement domain and 7.5% in the math domain.