MFTCX plain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLM s through Multi-hop Hate Speech Explanation (2025)

Abstract: Ensuring the moral reasoning capabilities of Large Language Models (LLMs) is a growing concern as these systems are used in socially sensitive tasks. Nevertheless, current evaluation benchmarks present two major shortcomings: a lack of annotations that justify moral classifications, which limits transparency and interpretability; and a predominant focus on English, which constrains the assessment of moral reasoning across diverse cultural settings. In this paper, we introduce MFTCXplain, a multilingual benchmark dataset for evaluating the moral reasoning of LLMs via multi-hop hate speech explanations using the Moral Foundations Theory. MFTCXplain comprises 3,000 tweets across Portuguese, Italian, Persian, and English, annotated with binary hate speech labels, moral categories, and text span-level rationales. Our results show a misalignment between LLM outputs and human annotations in moral reasoning tasks. While LLMs perform well in hate speech detection (F1 up to 0.836), their ability to predict moral sentiments is notably weak (F1 < 0.35). Furthermore, rationale alignment remains limited mainly in underrepresented languages. Our findings show the limited capacity of current LLMs to internalize and reflect human moral reasoning.

## introduction large language models (llms) perform tasks that rely on moral reasoning, from moderating harmful content to providing an ethical advisor @xcite . however, recent research has raised concerns about the robustness, explainability, and alignment of llms with diverse human values @xcite @xcite @xcite , as they often exhibit inconsistent behavior, lack transparency, and reflect culturally contingent moral frameworks. one domain where moral reasoning is especially prominent is hate speech. people often use moral language (e.g., purity, loyalty, and authority) to justify incivility or frame out-group harm as morally righteous @xcite . such moral framings of hate can trigger moral outrage, increase engagement, and reinforce group loyalty @xcite @xcite . measuring this form of moral reasoning is crucial, as different moral appeals shape hate in culturally and psychologically distinct ways @xcite . despite advances in hate speech detection that address linguistic and social bias @xcite @xcite , models often remain "black-box" systems, produc-ing classifications without revealing their underlying reasoning @xcite . furthermore, most existing benchmarks are english-only, limiting the development of models capable of culturally grounded moral reasoning @xcite . to address these challenges, we introduce mftcxplain (moral foundations twitter corpus with explanations), the first multilingual benchmark dataset for evaluating moral reasoning in llms using expert-annotated rationales based on the moral foundations theory (mft) @xcite . the dataset comprises 3,000 tweets in four languages (portuguese, persian, italian, and english) annotated for hate speech, finegrained moral categories, and text spans (rationales) that justify each moral label. notably, persian and brazilian portuguese represent the global south, low-resource contexts often neglected in nlp research, while italian, though not from the global south, remains understudied. annotations have been conducted by native speakers with diverse cultural backgrounds. we provide detailed annotator metadata to support bias analysis. while multi-hop explanation has traditionally been studied in question answering @xcite @xcite and fact verification @xcite , we draw on this paradigm to conceptualize moral explanation as a multi-hop reasoning process (see figure 1 ). this requires llms to connect expressions of hate with moral sentiments through intermediate inferences, such as identifying hate speech (offensive language used against targeted groups) -hop 1; recognizing moral violations -hop 2; and their rationales -hop 3; thus requiring multi-level moral reasoning and compositional inference. to evaluate current model capabilities, we benchmark state-of-the-art llms, including gpt-4o and llama 3.3 70b, using zero-shot, few-shot, and chain-of-thought (cot) prompting. our results reveal a substantial divergence between human-and model-generated explanations and low macro f1 scores (often below 0.35) when predicting moral sentiment, highlighting the challenges llms face in aligning with culturally human moral reasoning. the dataset, including annotation agreements and disagreements, model output, and code, is publicly available to facilitate future research. our contributions are summarized as follows: (i) we provide the first expert-annotated multilingual dataset of multi-hop hate speech explanation for evaluating llms' moral reasoning; (ii) we propose a novel annotation schema based on the mft to enhance the interpretability of hate speech detection models by multi-hop explanation; (iii) we conduct the first multilingual study on explainable moral reasoning in llms, revealing insights across cultures and four languages. ## corpus analysis we observe differences in the distribution of the number of words across categories and languages. cheating is the category with a relatively high number of total words (i.e., more than 10% of the total) across all four languages. in contrast, purity has a relatively low number of words (less than 2% of the total). harm is well represented in all languages, especially in english and persian subcorpora, but less so in portuguese, where it accounts for less than 7% of the total words. the italian data shows a higher number of words related to subversion and degradation compared to the other languages, while the persian data shows a relatively high number of words referring to authority. finally, a specific feature of the portuguese data is the higher number of words for the classes fairness, betrayal, and loyalty. ## experimental setup to measure how state-of-the-art llms align with the moral values and hate speech labels annotated by our experts, we utilized the technique of llms as objective judges @xcite @xcite , and extended it with a multi-hop explanation evaluation strategy. this approach requires models not only to predict the labels of the input text, but also to infer the corresponding moral violation, with a brief evidence-based justification, specifying the rationales or group of words that triggered the moral value classification. we selected llama-70b-instruct foot_1 and gpt-4o mini foot_2 , accessing them all via api. the prompting experiments were conducted on google colab using the cpu for processing, with the macro f1 score metric from sklearn library. llama-70b-instruct was chosen for its established effectiveness in similar evaluations @xcite while gpt-4o-mini provides an excellent balance of performance and cost-efficiency (nezhad and agrawal, 2025). moreover, using both models enables for a comparative analysis between an open (llama) and closed-source model (gpt-4o-mini). parameter settings remained consistent across both models, with each being tasked with determining: (i) whether the tweet comprises hate speech; (ii) which moral sentiments are present in the tweet; (iii) offering rationales for the moral sentiment prediction via the relevant text spans extracted from the tweet. to obtain comprehensive assessments, we implemented three distinct prompting strategies: (i) zeroshot prompting with no examples provided, asking the model to make determinations based solely on its knowledge; (ii) few-shot prompting with four examples included to help guide the model behavior; and chain-of-thought (cot) strategy, with a step-by-step reasoning approach aimed at guiding the models' reasoning. the examples of prompts are shown in the appendix d. for each language, the four examples randomly selected for the few-shot prompting condition were removed from the datasets used in the zero-shot and cot prompting conditions to allow a fair comparison. ## evaluation for the final outcome, we obtain multi-hop explanations that include the hate label, the moral sentiments, and the rationales for those moral sentiments, all predicted by the llms foot_3 . in max pairwise jaccard (see equation 1 ), for each instance, the jaccard similarity is calculated between the predicted rationale and each human annotated rationale, keeping the maximum value. the final score is the average of these maximum values in all cases. this aligns with the reasoning of "best human-llm pair" per instance. in bidirectional jaccard (see equation 2 ), we compute the average of the jaccard similarity in both directions: from predicted to annotated tokens, and from annotated to predicted tokens. this approach penalizes imbalances between precision and recall in token selection. in max pairwise bertscore (see equation 3 ), for each span generated by the llm, it compares the llm rationales against all human-annotated spans and retains the highest score per instance. in bidirectional bertscore (see equation 4 ), as the average of the bertscore computed in both directions: from the llm-predicted rationale to the human-annotated rationale (avgmax @xmath0, and from the human-annotated rationale to the llm-predicted rationale (avgmax @xmath1. it captures semantic precision and recall, even with different wordings. ## results and discussion results are shown in in particular, italian appears to be the most accessible language for both models, especially for gpt-4o, which achieves its highest moral f1 (0.440) in this language under cot prompting. in contrast, persian and portuguese consistently show lower performance, reflecting the challenges that even state-of-the-art llms face when reasoning over underrepresented languages foot_6 . when evaluating the alignment between modelgenerated rationales and human annotations, performance varies widely across languages and prompting strategies. despite moderate lexical overlaps in jaccard metrics (e.g., jaccard maxpair reaching up to 0.565 for llama-70b cot on pt), semantic similarity metrics such as bertscore suggest a deeper gap in the models' capacity to reproduce human moral justifications. for example, gpt-4o under cot prompting achieves a jaccard of only 0.099 and bertscore of 0.198 for portuguese, revealing a severe misalignment in token-level rationales. interestingly, while few-shot prompting was expected to improve alignment, it did not consistently outperform zero-shot or cot prompting across tasks, particularly for rationale generation. these findings highlight the limitations of current llms in generating culturally-aware moral explanations and point to the need for high-quality, multilingual moral annotated data as the proposed mftcxplain 10 . finally, according to the cross-cultural corpus analysis, hate speech consistently comprises moralized content. across english, italian, portuguese, and persian, tweets labeled as hate speech rarely appeared as "non-moral" and frequently relied on moral foundations such as harm, degradation, fairness, and loyalty. this supports prior findings that hate speech is often framed by moral sentiments @xcite . ## conclusion this paper introduces mftcxplain, the first multilingual benchmark designed to evaluate the moral reasoning of large language models through structured, multi-hop explanations of hate speech grounded in moral foundations theory. by incorporating expert-annotated rationales across four linguistically and culturally diverse languages, our dataset enables a fine-grained and interpretable analysis of moral reasoning in llms. empirical findings reveal significant misalignments between llm-generated justifications and human moral reasoning, underscoring the limitations of current llm reasoning techniques. our cross-cultural corpus analysis also reveals that hate speech is deeply moralized and is rarely used without moral sentiment. mftcxplain thus provides not only a new approach for assessing model behavior, but also a