Search Results for author:
Found 16 papers, 10 papers with code
Combining Open Domain Question Answering with a Task-Oriented Dialog System
The aim of this system is to combine the strength of an open-domain question answering system with the conversational power of task-oriented dialog systems.
An Annotated Corpus of Textual Explanations for Clinical Decision Support
In recent years, machine learning for clinical decision support has gained more and more attention.
Explainable Artificial Intelligence (XAI)
Feature Importance
Towards Personality-Aware Chatbots
Chatbots are increasingly used to automate operational processes in customer service.
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem
Additionally, we conduct an ablation study to assess the importance of feedback and suggestions.
Free-text Rationale Generation under Readability Level Control
Free-text rationales justify model decisions in natural language and thus become likable and accessible among approaches to explanation across many tasks.
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems
Conversational explainable artificial intelligence (ConvXAI) systems based on large language models (LLMs) have garnered significant interest from the research community in natural language processing (NLP) and human-computer interaction (HCI).
LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations
Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding (Slack et al., 2023; Shen et al., 2023), as one-off explanations may fall short in providing sufficient information to the user.
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations
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.
Inseq: An Interpretability Toolkit for Sequence Generation Models
Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools.
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods
Saliency maps can explain a neural model's predictions by identifying important input features.
Mediators: Conversational Agents Explaining NLP Model Behavior
The human-centric explainable artificial intelligence (HCXAI) community has raised the need for framing the explanation process as a conversation between human and machine.
Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools
To facilitate research, we present Thermostat which consists of a large collection of model explanations and accompanying analysis tools.
European Language Grid: A Joint Platform for the European Language Technology Community
Europe is a multilingual society, in which dozens of languages are spoken.
Efficient Explanations from Empirical Explainers
Amid a discussion about Green AI in which we see explainability neglected, we explore the possibility to efficiently approximate computationally expensive explainers.
Evaluating German Transformer Language Models with Syntactic Agreement Tests
Most of the studies were conducted for the English language, however.
Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows.
