5 papers
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
Shaghayegh Kolli, Richard Rosenbaum, Timo Cavelius +3
Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time, knowledge-graph-based fact-checkers de…
StylisticBias: A Few Human Visual Cues Drive Most Social Biases in MLLMs
Shaghayegh Kolli, Timo Cavelius, Nafiseh Nikeghbal +2
Multimodal large language models (MLLMs) are increasingly deployed in personally and societally consequential settings, yet the visual cues that shape how these models judge people…
Who Flips? Self- and Cross-Model Counterarguments Reveal Answer Instability in LLMs
Nafiseh Nikeghbal, Amir Hossein Kargaran, Shaghayegh Kolli +1
Standard accuracy benchmarks are designed to test how closely large language models (LLMs) approach correct answers, but are not suitable for testing whether LLMs stick with a corr…
Neuron-Level Interventions for Gendered and Gender-Neutral Generation in Language Models
Zhiwen You, Nafiseh Nikeghbal, Jana Diesner
Language models (LMs) can produce gendered language and stereotypes even when given neutral prompts. Most prior work on gender bias in LMs primarily examines gender through a binar…
GlotOCR Bench: OCR Models Still Struggle Beyond a Handful of Unicode Scripts
Amir Hossein Kargaran, Nafiseh Nikeghbal, Jana Diesner +2
Optical character recognition (OCR) has advanced rapidly with the rise of vision-language models, yet evaluation has remained concentrated on a small cluster of high- and mid-resou…