Papers by Tiziano Labruna
PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation (2025.acl-long)
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| Challenge: | Psychological studies have shown that infusing persuasion knowledge enhances disinformation detection. |
| Approach: | They introduce a persuasion-augmented chain of thought approach that leverages persulasion to improve disinformation detection in zero-shot classification. |
| Outcome: | The proposed approach outperforms competitive methods by 15% on online news and social media posts. |
MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection (2026.eacl-long)
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Arkadiusz Modzelewski, Witold Sosnowski, Eleni Papadopulos, Elisa Sartori, Tiziano Labruna, Giovanni Da San Martino, Adam Wierzbicki
| Challenge: | Existing studies on intentionality behind disinformation do not address intent behind disinformative agents. |
| Approach: | They propose an intent-augmented reasoning system that integrates intent analysis to mitigate the persuasive impact of disinformation. |
| Outcome: | The proposed corpus is the first human-annotated English corpus to capture disinformation and its malicious intent. |
Detecting Winning Arguments with Large Language Models and Persuasion Strategies (2026.findings-eacl)
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| Challenge: | Recent studies have focused on predicting winning arguments, i.e., those that effectively convince a reader to adopt a certain opinion. |
| Approach: | They propose to use large language models with a chain-of-thought framework to guide reasoning over six persuasion strategies to determine persuasiveness. |
| Outcome: | The proposed approach leverages large language models with a chain-of-thought framework that guides reasoning over six persuasion strategies. |
Addressing Domain Changes in Task-oriented Conversational Agents through Dialogue Adaptation (2023.eacl-srw)
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| Challenge: | Recent task-oriented dialogue systems are trained on annotated dialogues, but when domain knowledge changes, the initial model may become obsolete. |
| Approach: | They propose to use an annotated dialogue dataset to train a dialogue model for domain changes . they propose to fine-tune a generative language model on domain changes to reduce performance . |
| Outcome: | The proposed approach reduces performance by 55% by fine-tuning a generative language model on domain changes. |
Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation (2024.lrec-main)
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| Challenge: | Current task-oriented dialogue systems are trained on a single conversational style and do not account for the diversity of styles encountered when interacting with different users. |
| Approach: | They propose a method for generating multi-style conversations using a multi-language dataset that is available in a conversational domain. |
| Outcome: | The proposed model can be used in the development of conversational agents . it assumes the availability of a conversational domain and leverages the generative capabilities of large language models. |
Dynamic Task-Oriented Dialogue: A Comparative Study of Llama-2 and Bert in Slot Value Generation (2024.eacl-srw)
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| Challenge: | Recent advances in instruction-based language models have demonstrated exceptional performance across various natural language processing tasks. |
| Approach: | They propose to use BERT and Llama-2 to generate dynamic task-oriented dialogues using a multi-dimensional dataset. |
| Outcome: | The proposed models generate predictions for masked slot values within text and are reproducible in open-source environments. |