Culturally Adaptive Explainable LLM Assessment for Multilingual Information Disorder: A Human-in-the-Loop Approach
May 12, 2026·
·
0 min read
Maziar Kianimoghadam Jouneghani
Abstract
Recognizing information disorder is difficult because judgments about manipulation depend on cultural and linguistic context. Yet current Large Language Models (LLMs) often behave as monocultural, English-centric “black boxes,” producing fluent rationales that overlook localized framing. Preliminary evidence from the multilingual Information Disorder (InDor) corpus suggests that existing models struggle to explain manipulated news consistently across communities. To address this gap, this ongoing study proposes a Hybrid Intelligence Loop, a human-in-the-loop (HITL) framework that grounds model assessment in human-written rationales from native-speaking annotators. The approach moves beyond static target-language few-shot prompting by pairing English task instructions with dynamically retrieved target-language exemplars drawn from filtered InDor annotations through In-Context Learning (ICL). In the initial pilot, the Exemplar Bank is seeded from these filtered annotations and used to compare static and adaptive prompting on Farsi and Italian news. The study evaluates span and severity prediction, the quality and cultural appropriateness of generated rationales, and model alignment across evaluator groups, providing a testbed for culturally grounded explainable AI.
Type
Publication
Presented at the Information Disorder Workshop (InDor26) at LREC 2026, Palma de Mallorca, Spain (Non-archival)
Authors
PhD Candidate | NLP Researcher
Hi! My name is Maziar, though you can call me Maaz. I’m currently a PhD candidate in Computational Sciences and Technological Innovation, specializing in Artificial Intelligence and Data Science, at the DISIT department, University of Eastern Piedmont (UPO). I’m part of the ARC2S group, working under the supervision of Prof. Giancarlo Ruffo and Prof. Anna Sapienza. I finished my Master’s in Language Technologies and Digital Humanities at the University of Turin in July 2026.
My research over the past two years has centered on multilingual and cross-cultural NLP, and on harmful online content, specifically how it spreads and how it is perceived differently across cultures and communities.
This interest comes from my background. I grew up bilingual in Persian and Qashqai Turkish (a Turkic language of the Oghuz branch), and then did my bachelor’s in English Language Translation, with a focus on linguistics and translation studies. This is the starting point of my interest in meaning, cultures, and languages. We didn’t have interdisciplinary NLP master’s program in my country that I could join with that background (and we still don’t!), so I applied abroad and came to Italy to study NLP.
My master’s thesis, supervised by Prof. Viviana Patti and Dr. Marco Antonio Stranisci, builds on a community-developed dataset on information disorder, InDor. The idea was that an LLM’s fluent reasoning about manipulative content is not necessarily culturally aligned. So I thought maybe retrieving items and explanations from native speakers based on semantic similarity (instead of static few-shot) could fill this gap a bit. I built this retrieval framework and tested it on Persian and Italian, with automatic metrics, a qualitative analysis, and a human evaluation study.
In general, my research interests are: Multilingual & Cross-Cultural NLP, Computational Social Science (CSS), Explainable AI, Fairness, In-Context Learning, Human-in-the-Loop AI, and Information Disorder.
Beyond academia, I spent over 3 years in SEO, web content, and digital marketing.
My research over the past two years has centered on multilingual and cross-cultural NLP, and on harmful online content, specifically how it spreads and how it is perceived differently across cultures and communities.
This interest comes from my background. I grew up bilingual in Persian and Qashqai Turkish (a Turkic language of the Oghuz branch), and then did my bachelor’s in English Language Translation, with a focus on linguistics and translation studies. This is the starting point of my interest in meaning, cultures, and languages. We didn’t have interdisciplinary NLP master’s program in my country that I could join with that background (and we still don’t!), so I applied abroad and came to Italy to study NLP.
My master’s thesis, supervised by Prof. Viviana Patti and Dr. Marco Antonio Stranisci, builds on a community-developed dataset on information disorder, InDor. The idea was that an LLM’s fluent reasoning about manipulative content is not necessarily culturally aligned. So I thought maybe retrieving items and explanations from native speakers based on semantic similarity (instead of static few-shot) could fill this gap a bit. I built this retrieval framework and tested it on Persian and Italian, with automatic metrics, a qualitative analysis, and a human evaluation study.
In general, my research interests are: Multilingual & Cross-Cultural NLP, Computational Social Science (CSS), Explainable AI, Fairness, In-Context Learning, Human-in-the-Loop AI, and Information Disorder.
Beyond academia, I spent over 3 years in SEO, web content, and digital marketing.