JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning
This addresses the problem of poor generalization, robustness, and controllability in instruction tuning for large language models, offering a novel method that could enhance model reliability in diverse applications.
The paper tackles the limitations of text-to-text instruction tuning (TextTuning) in large language models by introducing JsonTuning, a structure-to-structure approach using JSON to represent tasks, which improves generalization, robustness, and controllability. Results show JsonTuning consistently outperforms TextTuning across various benchmarks and scenarios.
Instruction tuning is vital for enhancing the performance of large language models (LLMs), but existing text-to-text methods, referred to as TextTuning, struggle with issues such as generalization, robustness, and controllability due to their lack of explicit task structures. We introduce JsonTuning, a structure-to-structure approach that uses JSON structures to represent tasks. This method improves generalization by clarifying task elements and their relations, boosts robustness by minimizing ambiguity, and enhances controllability by allowing precise control over outputs. We conduct an extensive comparative analysis between JsonTuning and TextTuning using various language models and benchmarks. Our findings reveal that JsonTuning consistently surpasses TextTuning in terms of performance, robustness, and controllability across different scenarios. By overcoming the limitations of TextTuning, JsonTuning demonstrates significant potential for developing more effective and reliable LLMs capable of handling diverse scenarios.