Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making

TL;DR

This paper introduces a two-stage framework called SADM that improves the reliability of rationales in NLP models while maintaining competitive task performance, addressing the disconnect between explanations and decisions.

Contribution

The paper presents a novel unified framework that enhances the alignment between generated rationales and model decisions in NLP tasks.

Findings

Improves the link between rationales and decisions in NLP models.

Achieves competitive task performance and rationale quality.

Effective in semi-supervised learning scenarios.

Abstract

Explaining black-box model behavior with natural language has achieved impressive results in various NLP tasks. Recent research has explored the utilization of subsequences from the input text as a rationale, providing users with evidence to support the model decision. Although existing frameworks excel in generating high-quality rationales while achieving high task performance, they neglect to account for the unreliable link between the generated rationale and model decision. In simpler terms, a model may make correct decisions while attributing wrong rationales, or make poor decisions while attributing correct rationales. To mitigate this issue, we propose a unified two-stage framework known as Self-Attribution and Decision-Making (SADM). Through extensive experiments on five reasoning datasets from the ERASER benchmark, we demonstrate that our framework not only establishes a more reliable link between the generated rationale and model decision but also achieves competitive results in task performance and the quality of rationale. Furthermore, we explore the potential of our framework in semi-supervised scenarios.