7 papers
Redirected, Not Removed: Task-Dependent Stereotyping Reveals the Limits of LLM Alignments
Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur +3
How biased is a language model? The answer depends on how you ask. A model that refuses to choose between castes for a leadership role will, in a fill-in-the-blank task, reliably a…
SocioEval: A Template-Based Framework for Evaluating Socioeconomic Status Bias in Foundation Models
Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur +3
As Large Language Models (LLMs) increasingly power decision-making systems across critical domains, understanding and mitigating their biases becomes essential for responsible AI d…
Contract2Plan: Verified Contract-Grounded Retrieval-Augmented Optimization for BOM-Aware Procurement and Multi-Echelon Inventory Planning
Sahil Agarwal
Procurement and inventory planning is governed not only by demand forecasts and bills of materials (BOMs), but also by operational terms in contracts and supplier documents (e.g.,…
Quantifying CBRN Risk in Frontier Models
Divyanshu Kumar, Nitin Aravind Birur, Tanay Baswa +2
Frontier Large Language Models (LLMs) pose unprecedented dual-use risks through the potential proliferation of chemical, biological, radiological, and nuclear (CBRN) weapons knowle…
Beyond Text: Multimodal Jailbreaking of Vision-Language and Audio Models through Perceptually Simple Transformations
Divyanshu Kumar, Shreyas Jena, Nitin Aravind Birur +3
Multimodal large language models (MLLMs) have achieved remarkable progress, yet remain critically vulnerable to adversarial attacks that exploit weaknesses in cross-modal processin…
Beyond Western Politics: Cross-Cultural Benchmarks for Evaluating Partisan Associations in LLMs
Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur +3
Partisan bias in LLMs has been evaluated to assess political leanings, typically through a broad lens and largely in Western contexts. We move beyond identifying general leanings t…