8 citations · 14 across the 9 of their papers we have counts for
10 papers
ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety
Luke Bates, Max Glockner, Preslav Nakov +1
Conspiracy theories erode public trust in science and institutions while resisting debunking by evolving and absorbing counter-evidence. As AI-generated misinformation becomes incr…
NeoQA: Evidence-based Question Answering with Generated News Events
Max Glockner, Xiang Jiang, Leonardo F. R. Ribeiro +2
Evaluating Retrieval-Augmented Generation (RAG) in large language models (LLMs) is challenging because benchmarks can quickly become stale. Questions initially requiring retrieval…
Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks
Jing Yang, Max Glockner, Anderson Rocha +1
Free-text explanations are expressive and easy to understand, but many datasets lack annotated explanation data, making it challenging to train models for explainable predictions.…
Grounding Fallacies Misrepresenting Scientific Publications in Evidence
Max Glockner, Yufang Hou, Preslav Nakov +1
Health-related misinformation claims often falsely cite a credible biomedical publication as evidence. These publications only superficially seem to support the false claim, when l…
Missci: Reconstructing Fallacies in Misrepresented Science
Max Glockner, Yufang Hou, Preslav Nakov +1
Health-related misinformation on social networks can lead to poor decision-making and real-world dangers. Such misinformation often misrepresents scientific publications and cites…
Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation
Max Glockner, Yufang Hou, Iryna Gurevych
Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not…
AmbiFC: Fact-Checking Ambiguous Claims with Evidence
Max Glockner, Ieva Staliūnaitė, James Thorne +3
Automated fact-checking systems verify claims against evidence to predict their veracity. In real-world scenarios, the retrieved evidence may not unambiguously support or refute th…
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision
Max Glockner, Ivan Habernal, Iryna Gurevych
Evaluating the trustworthiness of a model's prediction is essential for differentiating between `right for the right reasons' and `right for the wrong reasons'. Identifying textual…
AdapterDrop: On the Efficiency of Adapters in Transformers
Andreas Rücklé, Gregor Geigle, Max Glockner +4
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements. Recent approaches tackle these shortco…
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences
Max Glockner, Vered Shwartz, Yoav Goldberg
We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI…