Is Explanation All You Need? An Expert Survey on LLM-generated Explanations for Abusive Language Detection
Authors: Chiara Di Bonaventura, Lucia Siciliani, Pierpaolo Basile, Albert Meroño-Peñuela, Barbara McGillivray (King's College London, Imperial College London, Università di Bari Aldo Moro, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Explainable abusive language detection has proven to help both users and content moderators, and recent research has focused on prompting LLMs to generate explanations for why a specific text is hateful. Yet, understanding the alignment of these generated explanations with human expectations and judgements is far from being solved. In this paper, we design a before-and-after study recruiting AI experts to evaluate the usefulness and trustworthiness of LLM-generated explanations for abusive language detection tasks, investigating multiple LLMs and learning strategies. Our experiments show that expectations in terms of usefulness and trustworthiness of LLM-generated explanations are not met, as their ratings decrease by 47.78% and 64.32%, respectively, after treatment. Further, our results suggest caution in using LLMs for explanation generation of abusive language detection due to (i) their cultural bias, and (ii) difficulty in reliably evaluating them with empirical metrics. In light of our results, we provide three recommendations to use LLMs responsibly for explainable abusive language detection.

Introduction
Explainability is a crucial open challenge in Natural Language Processing (NLP) research on abusive language AUTHOR as increasing models' complexity AUTHOR, models' intrinsic bias AUTHOR, and international regulations AUTHOR call for a shift in perspective from performance-based models to more transparent models. Moreover, recent studies have shown the benefits of explanations for users AUTHOR and content moderators AUTHOR on social media platforms. The former can benefit from receiving an explanation for why a certain post has been flagged or removed whereas the latter are shown to annotate toxic posts faster and solve doubtful annotations thanks to explanations. Several efforts have moved towards explainable abusive language detection in the past years, like the development of datasets containing rationales (i.e., the tokens in the text that suggest why the text is hateful) AUTHOR or implied statements (i.e., description of the implied meaning of the text) AUTHOR, and shared tasks on explainable hate speech detection AUTHOR, inter alia. With Large Language Models (LLMs) like FLAN-T5 AUTHOR showing remarkable performance across tasks and human-like text generation AUTHOR, recent studies have explored LLMs for explainable hate speech detection, wherein classification predictions are described through natural language explanations AUTHOR. For instance, AUTHOR used chain-of-thought prompting AUTHOR of LLMs to generate explanations for implicit hate speech detection. However, most of these studies rely on empirical metrics like BLEU AUTHOR to evaluate the generated explanations automatically. Consequently, the human perception and implications of these explanations remain understudied, as well as the extent to which empirical metrics approximate human judgements. AUTHOR recruited crowdworkers to evaluate the level of hatefulness in tweets and the quality of explanations generated by GPT-3. Instead, we conduct an expert survey investigating four LLMs and five learning strategies across multi-class abusive language detection tasks to answer the following questions: RQ1: How well do LLM-generated explanations for abusive language detection match human expectations? RQ2: How well do empirical metrics align with human judgements? RQ3: What makes LLM-generated explanations good, according to experts?

Conclusion
In this paper, we conducted a before-and-after study to understand human expectations and judgements of LLM-generated explanations for multi-class abusive language detection tasks. Contrarily to previous research AUTHOR, we investigated multiple LLMs and learning techniques, and we surveyed AI experts who are familiar with abusive language research instead of crowdworkers. We found that human expectations in terms of usefulness and trustworthiness of LLM-generated explanations are not met: after seeing these explanations, the usefulness and trustworthiness ratings decrease by 47.78% and 64.32%, respectively. Secondly, our results show that empirical metrics commonly used to evaluate textual explanations are highly volatile with respect to each other, even when they measure the same type of similarity (i.e., semantic vs. syntactic), and therefore pointing at the need of more reliable metrics for the empirical evaluation of textual explanations. In general, BERTScore and METEOR metrics exhibit the strongest correlation with human judgements. Lastly, our study provides evidence of the desiderata for LLM-generated explanations, suggesting that explanations should be coherent with human reasoning rather than model reasoning. Participants value the most textual explanations that are relevant and exhaustive to the text they refer to, while being logically and linguistically correct. Justifications for this preference lie on the fact that abusive language detection heavily relies on additional context and knowledge about slang and slurs, for which receiving an explanation is helpful to participants' understanding of the text. Future work should investigate whether this preference holds for other domains as well. In light of our findings, we conclude with three recommendations to use LLMs responsibly for explainable abusive language detection: (1) be aware of the cultural bias these models might exhibit when generating free-text explanations, which can further harm targeted groups; (2) if possible, instruction fine-tune LLMs for explanation generation of abusive language detection. This not only could ensure the generation of structured explanations as advised by previous research AUTHOR but it also returns the highest evaluation scores, both empirically and expert-wise, when using knowledge-guided prompts; (3) opt for a combination of empirical metrics to evaluate textual explanations when no human evaluation is possible, since no particular empirical metric seems to generalise across different learning techniques, models and datasets, making the ground-truth lie somewhere in between BERTScore (upper bound) and BLEU (lower bound). This work was supported by the UK Research and Innovation [grant number EP/S023356/1] in the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence (www.safeandtrustedai.org); by the Trustworthy AI Research award by The Alan Turing Institute, supported by the British Embassy Rome and the UK Science \& Innovation Network; and by the PNRR project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI (CUP H97G22000210007) under the NRRP MUR program funded by the NextGenerationEU. \appendix