8 papers
Talent or Luck? Evaluating Attribution Bias in Large Language Models
Chahat Raj, Mahika Banerjee, Jinhao Pan +3
When a student fails an exam, do we tend to blame their effort or the test's difficulty? Attribution, defined as how reasons are assigned to event outcomes, shapes perceptions, rei…
VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models
Chahat Raj, Bowen Wei, Aylin Caliskan +2
While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less attention. Existing VLM bias studies…
Metadata Conditioned Large Language Models for Localization
Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos
Large language models are typically trained by treating text as a single global distribution, often resulting in geographically homogenized behavior. We study metadata conditioning…
Graph Enhanced Trajectory Anomaly Detection
Jonathan Kabala Mbuya, Dieter Pfoser, Antonios Anastasopoulos
Trajectory anomaly detection is essential for identifying unusual and unexpected movement patterns in applications ranging from intelligent transportation systems to urban safety a…
TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla
Nishat Raihan, Antonios Anastasopoulos, Marcos Zampieri
Despite being the 5th most spoken language, Bangla remains underrepresented in Large Language Models (LLMs), particularly for code generation. This primarily stems from the scarcit…
mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation
Nishat Raihan, Antonios Anastasopoulos, Marcos Zampieri
Recent advancements in large language models (LLMs) have significantly enhanced code generation from natural language prompts. The HumanEval Benchmark, developed by OpenAI, remains…