Context-dependent Instruction Tuning for Dialogue Response Generation
This addresses a bottleneck in dialogue systems for more effective and efficient response generation, though it is incremental as it builds on existing instruction tuning methods.
The paper tackles the problem of instruction tuning for multi-turn dialogue generation, where fixed task instructions are ineffective due to varying context, and introduces a context-based framework that generates both instructions and responses, achieving comparable or outstanding results on benchmark datasets with reduced computation.
Recent language models have achieved impressive performance in natural language tasks by incorporating instructions with task input during fine-tuning. Since all samples in the same natural language task can be explained with the same task instructions, many instruction datasets only provide a few instructions for the entire task, without considering the input of each example in the task. However, this approach becomes ineffective in complex multi-turn dialogue generation tasks, where the input varies highly with each turn as the dialogue context changes, so that simple task instructions cannot improve the generation performance. To address this limitation, we introduce a context-based instruction fine-tuning framework for each multi-turn dialogue which generates both responses and instructions based on the previous context as input. During the evaluation, the model generates instructions based on the previous context to self-guide the response. The proposed framework produces comparable or even outstanding results compared to the baselines by aligning instructions to the input during fine-tuning with the instructions in quantitative evaluations on dialogue benchmark datasets with reduced computation budget.