I nstruct D ial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022)

Abstract: Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Dialogue is an especially interesting area in which to explore instruction tuning because dialogue systems perform multiple kinds of tasks related to language (e.g., natural language understanding and generation, domain-specific interaction), yet instruction tuning has not been systematically explored for dialogue-related tasks. We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. We explore cross-task generalization ability on models tuned on InstructDial across diverse dialogue tasks. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting. To ensure that models adhere to instructions, we introduce novel meta-tasks. We establish benchmark zero-shot and few-shot performance of models trained using the proposed framework on multiple dialogue tasks.

## introduction pretrained large language models (llms) @xcite @xcite are not only few-shot learners, but can also perform numerous language tasks without the need for fine-tuning. however, llms are expensive to train and test. instruction tuning has emerged as a tool for directly inducing zero-shot generalization on unseen tasks in language models by using natural language instructions @xcite @xcite figure 1 : we investigate instruction tuning on dialogue tasks. instruction tuning involves training a model on a mixture of tasks defined through natural language instructions. instruction tuned models exhibit zero-shot or few-shot generalization to new tasks. 2022). natural language instructions can contain components such as task definitions, examples, and prompts which allows them to be customized for multitask learning. instruction tuning enables developers, practitioners, and even non-expert users to leverage language models for novel tasks by specifying them through natural language, without the need for large training datasets. furthermore, instruction tuning can work for models that are significantly smaller than llms @xcite , making them more practical and affordable. most recent work @xcite @xcite on instruction tuning has focused on general nlp tasks such as paraphrase detection and reading comprehension, but not specifically on dialogue. while some work such as @xcite include a few dialogue tasks, those tasks are collected through crowdsourcing and do not provide good coverage of dialogue tasks and domains. no prior work has examined how training a model on a wide range of dialogue tasks with a variety of instructions may affect a system's ability to perform on both core dialogue tasks such as intent detection and response generation, and domain-specific tasks such as emotion classification. in this work, we introduce instructdial, a framework for instruction tuning on dialogue tasks. we provide a large curated collection of 59 dialogue datasets and 48 tasks, benchmark models, and a suite of metrics for testing the zero-shot and few-shot capabilities of the models. instruct-dial consists of multiple dialogue tasks converted into a text-to-text format (figure 1 ). these dialogue tasks cover generation, classification, and evaluation for both task-oriented and open-ended settings and are drawn from different domains (figure 2 ). instruction tuned models may ignore instructions and attain good performance with irrelevant prompts @xcite , without actually following user's instructions. we address this issue in two ways: (1) we train the models with a variety of outputs given the same input context by creating multiple task formulations, and (2) we propose two instruction-specific meta-tasks (e.g., select an instruction that matches with an inputoutput pair) to encourage models to adhere to the instructions. the main contributions of this work are: our experiments reveal further room for improvement on issues such as sensitivity to instruction wording and task interference. we hope that instructdial will facilitate further progress on instruction tuning for dialogue tasks. ## methodology in this section, we first discuss instruction tuning setup. next, we discuss the taxonomy of dialogue tasks, the task meta-information schema, and discuss how dialogue datasets and tasks are mapped into our schema. finally, we discuss model training and fine-tuning details. ## experiments and results we evaluate our models on multiple zero-shot and few-shot settings. we establish benchmark results for zero-shot unseen tasks evaluation (section 5.1) and response evaluation task (section 5.2) and perform error analysis. next, we perform zero-shot and few-shot experiments on three important dialogue tasks: intent detection, slot value generation, and dialogue state tracking (section 5.3). ## conclusion we propose instructdial, an instruction tuning framework for dialogue, which contains multiple dialogue tasks created from openly available dialogue datasets. we also propose two meta-tasks to encourage the model to pay attention to instructions. our results show that models trained on in-structdial achieve good zero-shot performance on unseen tasks (e.g., dialogue evaluation) and good few-shot performance on dialogue tasks (e.g., intent prediction, slot filling). we perform ablation studies showing the impact of using an instruction tuned base model, model size/type, increasing the number of tasks, and incorporating our proposed meta tasks. our experiments reveal that instruction tuning does not benefit all unseen test tasks and that improvements can be made in instruction wording invariance and task interference. we hope that instructdial will facilitate further progress on instruction-tuning systems for dialogue tasks.