3 citations · 5 across the 8 of their papers we have counts for
9 papers
Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness
Pardis Sadat Zahraei, Janvijay Singh, Gokhan Tur +1
Large language models are characterized by three key properties: capability, alignment, and faithfulness. Prior work studies the tradeoffs between capability and alignment, and bet…
AgentAbstain: Do LLM Agents Know When Not to Act?
Xun Liu, Yi Evie Zhang, Vira Kasprova +5
Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents…
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…
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…
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations
Pardis Sadat Zahraei, Ali Emami
Addressing gender bias and maintaining logical coherence in machine translation remains challenging, particularly when translating between natural gender languages, like English, a…
Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions
Mohammad Mahdi Abootorabi, Omid Ghahroodi, Pardis Sadat Zahraei +17
Generative AI is reshaping art, gaming, and most notably animation. Recent breakthroughs in foundation and diffusion models have reduced the time and cost of producing animated con…
Detecting Bias and Enhancing Diagnostic Accuracy in Large Language Models for Healthcare
Pardis Sadat Zahraei, Zahra Shakeri
Biased AI-generated medical advice and misdiagnoses can jeopardize patient safety, making the integrity of AI in healthcare more critical than ever. As Large Language Models (LLMs)…
TuringQ: Benchmarking AI Comprehension in Theory of Computation
Pardis Sadat Zahraei, Ehsaneddin Asgari
We present TuringQ, the first benchmark designed to evaluate the reasoning capabilities of large language models (LLMs) in the theory of computation. TuringQ consists of 4,006 unde…
WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Experts
Pardis Sadat Zahraei, Ali Emami
The Winograd Schema Challenge (WSC) serves as a prominent benchmark for evaluating machine understanding. While Large Language Models (LLMs) excel at answering WSC questions, their…