Language Model Training: Unlocking Skills and Enhancing Performance
Large language models (LMs) have gained recognition for their remarkable ability to author source code, create original art, and engage in conversations. These capabilities are a result of the extensive data used to train these models. However, selecting the most beneficial data for training remains a challenge, as existing algorithms rely on heuristics rather than a formal framework.
Inspired by how humans learn, researchers sought to create a framework that links data to LM training and behavior. They explored the concept of skill orderings, drawing from educational literature that suggests presenting concepts in a specific sequence enhances learning. If similar orderings exist in LM training, they could offer a deeper understanding and more efficient training approach.
To develop this framework, two key issues needed resolution. First, an operational definition of LM skill and skill order was essential. Semantic groupings of data were considered, but they proved insufficient. The team identified the need to define skills based on sample distributions for optimal model training. They also noted the importance of considering the imbalance and ordering of abilities.
The researchers introduced the concept of a skill as a unit of behavior that an LM can learn through specific training data. They defined an ordered skill set as a group of skills with a directed graph, where prerequisite skills can accelerate the learning process. Notably, they found that learning a specific skill rapidly requires training on both that skill and its necessary prerequisites.
Building on these findings, the researchers proposed two approaches for selecting training data that facilitate skill acquisition. The first approach, skill-stratified sampling, focuses on uniformly sampling relevant skills to address the issue of unevenly distributed skills. However, it does not consider the training progression and may oversample previously acquired abilities.
To overcome this limitation, the researchers developed an online data selection technique called SKILL-IT. This technique assigns higher weight to yet-to-be-learned skills or influential prerequisite skills. SKILL-IT leverages the link between assessment skills and training skills to optimize loss minimization in various training scenarios, such as pre-training and fine-tuning.
Experimental evaluations on both artificial and actual datasets validated the effectiveness of SKILL-IT. Compared to random selection and curriculum learning, SKILL-IT demonstrated significant improvements in accuracy and loss reduction. Furthermore, when applied to the RedPajama 1.2 trillion token dataset, SKILL-IT outperformed uniform sampling in terms of accuracy.
The introduction of this framework and the SKILL-IT algorithm showcase the potential for data-efficient language model training. By understanding the relationship between skills, their order, and the data used, researchers and developers can enhance LM performance effectively. The insights gained from this research contribute to the ongoing advancement of large language models and their applications across various domains.
In conclusion, a team of researchers has explored the use of skill orderings and skill-based data selection for language model training. By defining skills as units of behavior and developing a framework that considers their order and dependencies, they have introduced a new approach to enhance LM performance. The proposed SKILL-IT algorithm offers a data selection strategy that optimizes learning by assigning weights to yet-to-be-acquired skills. Experimental results demonstrate the effectiveness of this approach and its potential to improve accuracy and reduce loss in language model training. This research significantly contributes to the understanding and development of large language models.
