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Understanding by Understanding Not: Modeling Negation in Language Models
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Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language models often handle negation incorrectly. To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus. By training BERT with the resulting combined objective we reduce the mean top~1 error rate to 4% on the negated LAMA dataset. We also see some improvements on the negated NLI benchmarks.
Forward citations
Cited by 3 Pith papers
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Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding
A new Korean benchmark shows language models underperform on negated sentences, and fine-tuning on it improves their negation understanding.
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Potemkin Understanding in Large Language Models
LLMs frequently pass definition questions yet fail to use the same concepts in classification, generation, and editing tasks, a gap the authors call potemkin understanding.
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