568 citations · 582 across the 15 of their papers we have counts for
6 papers · 1 filter
Same Chart, Different Story: Bias in Vision-Language Chart Interpretation
Mizanur Rahman, Huan Wu, Arash Asgari +2
Vision-language models (VLMs) are increasingly used to interpret charts and generate natural-language explanations for socially consequential data. However, they may produce differ…
LLMs Silently Correct African American English: Auditing and Mitigating Dialect Bias via Activation Steering
Huan Wu, Ali Emami, Muhammad Furquan Hassan +5
African American English (AAE), a rule-governed dialect spoken by over 30 million people, is routinely misinterpreted and "corrected" by large language models (LLMs). Across six in…
Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support
Mizanur Rahman, Abeer Badawi, Elahe Rahimi +4
Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive…
We Politely Insist: Your LLM Must Learn the Persian Art of Taarof
Nikta Gohari Sadr, Sahar Heidariasl, Karine Megerdoomian +2
Large language models (LLMs) struggle to navigate culturally specific communication norms, limiting their effectiveness in global contexts. We focus on Persian taarof, a social nor…
Template-Based Probes Are Imperfect Lenses for Counterfactual Bias Evaluation in LLMs
Farnaz Kohankhaki, D. B. Emerson, Jacob-Junqi Tian +2
Bias in large language models (LLMs) has many forms, from overt discrimination to implicit stereotypes. Counterfactual bias evaluation is a widely used approach to quantifying bias…
Soft-prompt Tuning for Large Language Models to Evaluate Bias
Jacob-Junqi Tian, David Emerson, Sevil Zanjani Miyandoab +3
Prompting large language models has gained immense popularity in recent years due to the advantage of producing good results even without the need for labelled data. However, this…