Adapting Pre-trained LLMs for Long Sequences

A research team at a small company has access to a powerful, general-purpose pre-trained language model. Their goal is to quickly develop a specialized application that can process and understand entire legal documents, which are significantly longer than the model's original training data. The team has limited time and computational resources for large-scale model training. Given these constraints, which of the following approaches represents the most practical and efficient research direction for them to pursue?

Developing Efficient Architectures and Training for Long-Sequence Self-Attention

Strategic Approaches to Long-Context Language Modeling

Preference for Adapting Standard Transformer Architectures

Comparing Strategies for Long-Context Language Modeling