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arXiv · 2307.13989

This is not correct! Negation-aware Evaluation of Language Generation Systems

Miriam Anschütz·Diego Miguel Lozano·Georg Groh
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Abstract

Large language models underestimate the impact of negations on how much they change the meaning of a sentence. Therefore, learned evaluation metrics based on these models are insensitive to negations. In this paper, we propose NegBLEURT, a negation-aware version of the BLEURT evaluation metric. For that, we designed a rule-based sentence negation tool and used it to create the CANNOT negation evaluation dataset. Based on this dataset, we fine-tuned a sentence transformer and an evaluation metric to improve their negation sensitivity. Evaluating these models on existing benchmarks shows that our fine-tuned models outperform existing metrics on the negated sentences by far while preserving their base models' performances on other perturbations.

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Miriam Anschütz, Diego Miguel Lozano, Georg Groh. 2023-07-26. This is not correct! Negation-aware Evaluation of Language Generation Systems. https://arxiv.org/abs/2307.13989

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