2 citations · 2 across the 10 of their papers we have counts for
12 papers · 1 filter
MAPLE: Metadata Conditioned LLM Pretraining for Locale-Aware Question Answering
Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos
Large language models can memorize competing locale-specific facts yet fail to select among them when the locale changes, defaulting instead to a single globally dominant answer. W…
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…
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…
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…
Breaking Bias, Building Bridges: Evaluation and Mitigation of Social Biases in LLMs via Contact Hypothesis
Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan +2
Large Language Models (LLMs) perpetuate social biases, reflecting prejudices in their training data and reinforcing societal stereotypes and inequalities. Our work explores the pot…