Papers by Matteo Guida

4 papers
MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Multi-hop Hate Speech Explanation (2025.findings-emnlp)

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Challenge: Existing evaluation benchmarks for large language models lack annotations that justify moral classifications and focus on English constrain moral reasoning across diverse cultural settings.
Approach: They propose a multilingual benchmark dataset for evaluating moral reasoning of large language models . it includes 3,000 tweets annotated with binary hate speech labels, moral categories and rationales .
Outcome: The proposed dataset shows a misalignment between LLM outputs and human annotations in moral reasoning tasks.
Self-Explaining Hate Speech Detection with Moral Rationales (2026.findings-acl)

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Challenge: Existing models for hate speech detection are opaque and rely on surface-level cues. Existing approaches often encode biases originating from training data and annotation processes.
Approach: They propose a framework that integrates moral rationale supervision into training . they propose SMRA for self-explaining hate speech detection .
Outcome: The proposed framework improves performance across binary hate speech detection and multi-label moral sentiment classification.
Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames (2026.findings-acl)

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Challenge: a computational framework allows to derive discourse metaphors through their source domains and semantic frames.
Approach: They propose a computational framework that allows to derive salient discourse metaphors through their source domains and semantic frames.
Outcome: The proposed framework uncovers well-known source domains and reveals nuanced frame-level associations that distinguish how the issue is portrayed.
Article and Comment Frames Shape the Quality of Online Comments (2026.findings-acl)

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Challenge: Recent work has focused on predicting comment toxicity or quality, but it ignores audience reactions.
Approach: They propose a frame-aware system to mitigate unhealthy discourse . they analysed 1M comments across 2.7K news articles .
Outcome: The proposed system can mitigate unhealthy discourses by analyzing 1M comments across 2.7K news articles.

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