Context-dependent Instruction Tuning for Dialogue Response Generation
Jin Myung Kwak Minseon Kim Sung Ju Hwang
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.