1 citations · 4 across the 20 of their papers we have counts for
8 papers · 2 filters
The Alignment Veto: How Safety Training Suppresses Cultural Knowledge in LLMs
Pardis Sadat Zahraei, Gokhan Tur, Dilek Hakkani-Tür +1
What happens inside a language model when alignment training conflicts with a cultural value it encodes? Across 16 MENA countries, 26 models, and 1.53M human survey responses, we s…
ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in Biographical Reasoning
Jasin Cekinmez, Omid Ghahroodi, Saad Fowad Chandle +2
We introduce ADAM (A Diverse Archive of Mankind), a framework for evaluating and improving multimodal large language models (MLLMs) in biographical reasoning. To the best of our kn…
EiCAP: Beyond Fluency, Probing and Improving Emotional Intelligence in LLMs via Psychologically Grounded Multi-Turn Dialogue
Nizi Nazar, Pardis Sadat Zahraei, Dilek Hakkani-Tür +2
Large Language Models increasingly serve in emotionally sensitive roles, including mental health support, education, and crisis response, yet they lack a principled framework for a…
ParsiPy: NLP Toolkit for Historical Persian Texts in Python
Farhan Farsi, Parnian Fazel, Sepand Haghighi +5
The study of historical languages presents unique challenges due to their complex orthographic systems, fragmentary textual evidence, and the absence of standardized digital repres…
MorphBPE: A Morpho-Aware Tokenizer Bridging Linguistic Complexity for Efficient LLM Training Across Morphologies
Ehsaneddin Asgari, Yassine El Kheir, Mohammad Ali Sadraei Javaheri
Tokenization is fundamental to Natural Language Processing (NLP), directly impacting model efficiency and linguistic fidelity. While Byte Pair Encoding (BPE) is widely used in Larg…
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation
Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani +5
Large Language Models (LLMs) suffer from hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented Generation (RAG) mitigates these i…
AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis
Alireza Ghahramani Kure, Mahshid Dehghani, Mohammad Mahdi Abootorabi +3
The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-…
AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations
Mohammad Mahdi Abootorabi, Nona Ghazizadeh, Seyed Arshan Dalili +3
In this study, we introduce a solution to the SemEval 2024 Task 10 on subtask 1, dedicated to Emotion Recognition in Conversation (ERC) in code-mixed Hindi-English conversations. E…