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QoEXplainer: Mediating Explainable Quality of Experience Models with Large Language Models
In this paper, we present QoEXplainer, a QoE dashboard for supporting humans in understanding the internals of an explainable, …
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DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data Perturbations and MinMax Training
The NLI4CT task at SemEval-2024 emphasizes the development of robust models for Natural Language Inference on Clinical Trial Reports …
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The Role of Explainability in Collaborative Human-AI Disinformation Detection
Manual verification has become very challenging based on the increasing volume of information shared online and the role of generative …
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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’ …
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Retrieval-Augmented Knowledge Integration into Language Models: A Survey
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A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of …
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Large Language Models Are Echo Chambers
Modern large language models and chatbots based on them show impressive results in text generation and dialog tasks. At the same time, …
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Assessing Authenticity and Anonymity of Synthetic User-generated Content in the Medical Domain
Since medical text cannot be shared easily due to privacy concerns, synthetic data bears much potential for natural language processing …
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Automatic User Experience Evaluation of Goal-Oriented Dialogs Using Pre- Trained Language Models
Dialog evaluation methods based on Pre-trained Language Models (Pr-LMs) have been primarily used for open-domain dialogs with the goal …
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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 …
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