InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations

TL;DR

InterroLang introduces an interactive dialogue system for NLP model explanations, enabling users to explore models and datasets through conversational interfaces, improving understanding and predictability of model behavior.

Contribution

This work adapts the TalkToModel framework to NLP, adding NLP-specific explanation operations and evaluating user interactions across multiple NLP tasks.

Findings

Rationalization and feature attribution aid understanding.

Dialogue explanations improve users' ability to predict model outcomes.

User studies show increased perceived helpfulness and correctness.

Abstract

While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface. Such a dialogue system can help users explore datasets and models with explanations in a contextualized manner, e.g. via clarification or follow-up questions, and through a natural language interface. We adapt the conversational explanation framework TalkToModel (Slack et al., 2022) to the NLP domain, add new NLP-specific operations such as free-text rationalization, and illustrate its generalizability on three NLP tasks (dialogue act classification, question answering, hate speech detection). To recognize user queries for explanations, we evaluate fine-tuned and few-shot prompting models and implement a novel Adapter-based approach. We then conduct two user studies on (1) the perceived correctness and helpfulness of the dialogues, and (2) the simulatability, i.e. how objectively helpful dialogical explanations are for humans in figuring out the model's predicted label when it's not shown. We found rationalization and feature attribution were helpful in explaining the model behavior. Moreover, users could more reliably predict the model outcome based on an explanation dialogue rather than one-off explanations.