1 citations · 1 across the 4 of their papers we have counts for
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Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders
Faeze Ghorbanpour, Shaghayegh Kolli, Jana Diesner +1
Multilingual dense retrievers place all languages in one shared space so that a question finds relevant documents, regardless of which language they are written in. We show that th…
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 evaluate whether large language models (LLMs) reach correct answers. However, they do not test whether models maintain that answer when challenged by a…
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…