Papers by Reza Farahbakhsh
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning (2025.coling-main)
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| Challenge: | Online abusive content detection, particularly in low-resource settings, remains underexplored. |
| Approach: | They propose to use pre-trained audio representations to detect abusive language in Indian languages using Few Shot Learning (FSL) . |
| Outcome: | The proposed model can be used to classify abusive language in 10 languages using the ADIMA dataset with FSL. |
No offence, Bert - I insult only humans! Multilingual sentence-level attack on toxicity detection networks (2023.findings-emnlp)
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| Challenge: | a new sentence-level attack on toxic detection models is shown to work on seven languages . toxicity detection systems are used to silence the voices of criticism, causing echo chambers . |
| Approach: | They propose a sentence-level attack that adds positive words to a hateful message . they show the attack works on seven languages from three different language families . |
| Outcome: | The proposed attack is shown to work on seven languages from three different language families. |
Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training (2024.lrec-main)
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| Challenge: | Recent studies improve cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. |
| Approach: | They propose to use negative pairs to align the multilingual model and self-train the model to converge on the obtained clean pseudo-labels. |
| Outcome: | The proposed method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods. |
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework (2026.acl-long)
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Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong, Thanh Trung Nguyen, Mai Hong Son, Reza Farahbakhsh, Phi Le Nguyen, Noel Crespi
| Challenge: | Current methods map whole volumes to reports, ignoring the clinical workflow of analyzing localized Regions of Interest (RoIs) Current models exhibit suboptimal accuracy and are prone to significant hallucinations. |
| Approach: | They propose a framework that mimics the professional radiologist diagnostic workflow by employing graph-based relational modules to capture dependencies between RoI attributes. |
| Outcome: | The proposed framework surpasses existing models by 19.7% in BLEU and 4.7% in ROUGE-L while achieving a 45.8% improvement in clinical metrics. |
The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs (2025.acl-long)
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| Challenge: | cipher decoding, riddles, code execution embedded into model prompts bypass safety safeguards of large language models (LLMs) . |
| Approach: | They introduce a novel class of adversarial jailbreak adversarials on large language models, termed Task-in-Prompt (TIP) attacks. |
| Outcome: | The proposed techniques circumvent safeguards in six state-of-the-art language models, including GPT-4o and LLaMA 3.2, and consistently generate restricted content . |