25 citations · 67 across the 43 of their papers we have counts for
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LLMs Do Not See Age: Assessing Demographic Bias in Automated Systematic Review Synthesis
Favour Yahdii Aghaebe, Tanefa Apekey, Elizabeth Williams +1
Clinical interventions often hinge on age: medications and procedures safe for adults may be harmful to children or ineffective for older adults. However, as language models are in…
Deconstructing Attention: Investigating Design Principles for Effective Language Modeling
Huiyin Xue, Nafise Sadat Moosavi, Nikolaos Aletras
The success of Transformer language models is widely credited to their dot-product attention mechanism, which interweaves a set of key design principles: mixing information across…
From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors
Maggie Mi, Aline Villavicencio, Nafise Sadat Moosavi
Language models often struggle with idiomatic, figurative, or context-sensitive inputs, not because they produce flawed outputs, but because they misinterpret the input from the ou…
More or Less Wrong: A Benchmark for Directional Bias in LLM Comparative Reasoning
Mohammadamin Shafiei, Hamidreza Saffari, Nafise Sadat Moosavi
Large language models (LLMs) are known to be sensitive to input phrasing, but the mechanisms by which semantic cues shape reasoning remain poorly understood. We investigate this ph…
MultiHoax: A Dataset of Multi-hop False-Premise Questions
Mohammadamin Shafiei, Hamidreza Saffari, Nafise Sadat Moosavi
As Large Language Models are increasingly deployed in high-stakes domains, their ability to detect false assumptions and reason critically is crucial for ensuring reliable outputs.…
Frame In, Frame Out: Measuring Framing Bias in LLM-Generated News Summaries
Valeria Pastorino, Nafise Sadat Moosavi
News headlines and summaries shape how events are interpreted through selective emphasis and omission, a phenomenon commonly referred to as framing. Large language models are now r…