This is not correct! Negation-aware Evaluation of Language Generation Systems (2023)

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.

## introduction previous work has shown that large language models such as bert @xcite lack understanding of negated phrases and do not attribute sufficient importance to the word "not" @xcite . nevertheless, many widely-used metrics to evaluate natural language generation (nlg) systems, such as bertscore @xcite or bleurt @xcite , are based on these models. automatic evaluation is indispensable when language models are published nearly every day. moreover, large benchmark datasets make a human evaluation of language models infeasible. therefore, metric scores are among the most important model selection criteria. however, here we see a severe issue when these metrics fail to distinguish between sentences and their negated versions. especially when considering fact-checking or entailment prediction, an uninterpreted "not" can invalidate the entire output of the model and, thus, reduce the trustworthiness of such systems. while there have been approaches to improve the negation-awareness in natural language in-reference: "ray charles is legendary." paraphrased: "ray charles is a legend." bleurt-20: 0.86 negbleurt: 0.84 negated: "ray charles isn't legendary." bleurt-20: 0.67 negbleurt: 0.27 ference (nli) or sentiment classification models @xcite , the task of negationsensitive evaluation of such systems is lacking behind @xcite . an example of such failure is shown in figure 1 , where a reference sentence is both paraphrased and negated. the wellestablished bleurt-20 metric @xcite gives a relatively high score of 0.67 to this negated sentence, suggesting that it does not fully capture the negation in the sentence. to extend negation research to the topic of evaluation, we present a negation-aware version of bleurt, named negbleurt (figure 1 ). in addition, we released a negation-aware sentence transformer @xcite based on an mpnet model @xcite ) that extends the application of negation sensitivity to a broader range of tasks. both models were fine-tuned on a labeled dataset with about 30 thousand sentence pairs in both their negated and paraphrased versions. we publish this dataset and the sentence negator used to create it together with our models. more specifically, our contributions are: our models were evaluated on various benchmark datasets showing that they greatly outperform their base model on negated sentences while delivering similar performance on other tasks. ## related work this section highlights existing work investigating the negation awareness of the bert language model and different nlg evaluation metrics. furthermore, we present approaches to improve this awareness with negation pre-training. ## contrastive negation dataset to make an evaluation metric aware of negations, we need a dataset containing pairs of reference and candidate sentences and a label of how well the candidate fits the reference. to have a balanced dataset, we not only need negated, but also meaning-preserving paraphrases of the reference sentence. as described in the previous section, there exist multiple datasets focusing on negations. however, most of these datasets are either targeted towards specific tasks such as nli or only contain negated sentence pairs. thus, we processed and aggregated the existent datasets producing a compilation of annotated, negation-oriented text-pairs (cannot), which addresses and solves these issues. more specifically, our negation-evaluation dataset is based on the following resources: to only focus on negations, we discarded sentence pairs that had a jaccard similarity coefficient of less than 0.55 or differed in length by four or more words. the word splits were obtained with simple white-space tokenization. after the processing, 14, 970 samples were kept. this resulted in a dataset with negated sentence pairs. to extend it with meaning-preserving paraphrases, we used a pegasus model pre-trained for this task foot_0 and created paraphrased versions of each of the references. finally, the dataset was augmented by adding a swapped version of each pair. this results in a dataset of 68, 780 sentence pairs with equal distribution of negated and equivalent samples. the pre-processed versions of the underlying datasets and our resulting dataset are publicly available on github foot_1 . ## negation-aware models we publish two different models fine-tuned on our cannot data. on the one hand, we fine-tuned a sentence transformer to return negation-aware sentence embeddings. while the cosine similarity of two embeddings can be applied as an evaluation metric, the embedding representations have broader use cases, e.g., for topic modeling @xcite . on the other hand, we fine-tuned bleurt @xcite , explicitly targeted towards the evaluation task. both models are published in our github repository foot_4 and on the hugging face model hub foot_5 . our negbleurt can also be utilized within the hugging face evaluate library @xcite 7 . ## evaluation a common issue with fine-tuning is catastrophic forgetting @xcite , i.e., the models forget their initial knowledge and overfit the new task. our fine-tuning approach is successful if it improves negation awareness while retaining performance on other tasks, e.g., not corrupting the detection quality of other errors in candidate sentences. we test our models on common embedding and evaluation benchmarks to test our approach and compare their performances against their respective base models. ## ablation study our cannot dataset consists of a diverse collection of datasets, as presented in (section 3). here, we perform an ablation study to measure the impact of the individual subsets on the model's overall performance. we individually removed each subset from the cannot-wmt training and evaluation data, and fine-tuned a new bleurt checkpoint based on the remaining subsets and the wmt data. the setup for training was the same as for negbleurt, which means we fine-tuned the bleurt-tiny checkpoint for 500 steps using the fine-tuning script from the bleurt github page. we did not modify the cannot-wmt test split, and thus, the number of samples in the subsets deviate from the numbers in section 3. to compare the impacts of the different datasets, we evaluated the fine-tuned models on all critical perturbations in the demetr evaluation benchmark @xcite . the model's sensitivities towards specific perturbations are presented in ## conclusion in this work, we created a sentence negation tool that we made available to the research community as a python package. in addition, we released cannot, a data collection for negation detection that can be used to improve negation awareness of language models. we leveraged this dataset to fine-tune a sentence transformer and an nlg evaluation metric. both models show a strong negation detection ability while preserving task-specific performances compared to their base models. considering the many papers that pointed out the negation weaknesses of model language models, our work is an important step towards negation awareness. in the future, we will extend our negator to support more advanced negations beyond the verb level and make our negation dataset multilingual so that multilingual nlg evaluation metrics can be improved as well.