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The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models.

, , , , and . EMNLP, page 5457-5476. Association for Computational Linguistics, (2023)

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The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models., , , , and . EMNLP, page 5457-5476. Association for Computational Linguistics, (2023)Language Model Re-rankers are Steered by Lexical Similarities., , , , , and . CoRR, (February 2025)What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge., and . ACL (student), page 252-261. Association for Computational Linguistics, (2022)A Reality Check on Context Utilisation for Retrieval-Augmented Generation., , , , , , , and . CoRR, (2024)How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?, and . COLING, page 5582-5596. International Committee on Computational Linguistics, (2022)CUB: Benchmarking Context Utilisation Techniques for Language Models., , , , , , and . CoRR, (May 2025)Knowledge Distillation for Swedish NER models: A Search for Performance and Efficiency., and . NoDaLiDa, page 124-134. Linköping University Electronic Press, Sweden, (2021)Transferring Knowledge from Vision to Language: How to Achieve it and how to Measure it?, , and . BlackboxNLP@EMNLP, page 149-162. Association for Computational Linguistics, (2021)Fact Recall, Heuristics or Pure Guesswork? Precise Interpretations of Language Models for Fact Completion., , , , and . CoRR, (2024)