To enhance the instruction-following capabilities of long-context language models (LLMs) for long-form text generation tasks, several strategies can be implemented:
Extended Instruction Tuning: Current instruction tuning datasets often consist of short prompts, typically under 200 tokens. By creating and utilizing longer instructional datasets that reflect the complexity and length of tasks expected in long-form generation, models can be better trained to handle extended contexts. This could involve synthesizing longer prompts that require multi-step reasoning and adherence to detailed instructions.
Hierarchical Instruction Structures: Implementing a hierarchical approach to instruction design can help models manage complex tasks more effectively. By breaking down long-form generation tasks into smaller, manageable subtasks with clear instructions, models can focus on completing each segment sequentially, thereby improving overall coherence and adherence to the original prompt.
Feedback Mechanisms: Incorporating feedback loops during the training phase can help models learn from their mistakes. By evaluating model outputs against expected results and providing corrective feedback, models can adjust their generation strategies to better align with user instructions.
Diverse Instruction Types: Expanding the variety of instruction types, such as Single, Range, and Periodic Instructions as seen in the SGT benchmark, can help models learn to interpret and execute different forms of directives. This diversity can enhance their flexibility and adaptability in generating long-form content.
Fine-tuning with Real-World Data: Utilizing real-world examples of long-form text generation, such as project proposals, creative writing, and technical documentation, can provide models with contextually rich data that reflects practical applications. This exposure can improve their ability to generate relevant and coherent outputs.