InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
(2205.12673)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. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especially interesting area 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. Next, 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.
Overview
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InstructDial is a novel framework designed for dialogue tasks that standardizes 48 diverse tasks using instruction tuning.
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The framework builds upon 59 dialogue datasets, resulting in a comprehensive, text-to-text format task repository.
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By introducing two meta-tasks, InstructDial aims to enhance model adherence to instructions, improving their behavior and performance.
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Experimental results show that InstructDial performs well in zero-shot scenarios and even better in few-shot settings.
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The study highlights room for improvement in areas such as instruction sensitivity and task interference while marking progress in dialogue system research.
Methodology
InstructDial, a novel framework presented by Gupta et al., systematically investigates instruction tuning within the realm of dialogue tasks. The methodology is founded upon the construction of a rich repository representing 48 diverse dialogue tasks in a unified text-to-text format. This task collection is derived from 59 openly available dialogue datasets, encompassing a broad spectrum of dialogue tasks. To enable models to adhere to diverse instructions, the authors propose two instruction-specific meta-tasks, encouraging correct instruction following.
Related Work
The framework is positioned against the backdrop of advanced pretraining methods and multi-task learning, elucidating how established transformer-based models have propelled dialogue systems forward. Previous endeavors have demonstrated the effectiveness of these models in both task-oriented dialogues (TOD) and open-domain conversations. Although significant progress has been made, particularly with models like DialoGPT, BlenderBot, and PLATO, comprehensive pretraining within dialogue systems across an extensive range of tasks remained uncharted territory until now.
Addressing Model Behavior and Performance
Concerns regarding model behaviors are intricately addressed by introducing variability in the outputs for a given input via multiple task formulations. Moreover, to bolster model adherence to instructions, two meta-tasks are suggested: instruction selection and instruction binary task. The primary contributions of this study include: the release of INSTRUCTDIAL, a framework open-sourced to facilitate the addition and configuration of new datasets and tasks, and the demonstration that instruction-tuned models enhance both zero-shot and few-shot performance across disparate dialogue tasks.
Experiments and Results
The experimental results are telling. The analysis reveals that INSTRUCTDIAL not only achieves good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection but also shows enhanced performance in few-shot settings. Further, through a series of ablation studies, the authors establish a strong link between the use of an instruction-tuned base model, prompt adherence, and the employment of proposed meta-tasks, thereby contributing to better generalization on unseen tasks. Despite the achievements, the study identifies potential for improvement on issues like the sensitivity to instruction wording and task interference. The proposed INSTRUCTDIAL framework thus marks a significant step toward instructive tuning for dialogue tasks, heralding new prospects for dialogue systems research.
The blog post digs into the technical details of "InstructDial," a new framework tailored for dialogue task instruction tuning, and examines its performance against contemporary benchmarks. Through a critical synthesis of methodology, related work, and detailed results, it anticipates the framework's influence on the progress of dialogue systems research.
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