2 citations · 2 across the 4 of their papers we have counts for
4 papers
ToolAlignBench: Investigating Alignment Conflicts in Tool-Calling Enabled LLMs
Aryan Keluskar, Amrita Bhattacharjee, Huan Liu
Safety alignment in LLMs aims to align models with human values, but which values take precedence when they conflict? We investigate this question in the context of tool-calling LL…
Evaluating Adaptive Personalization of Educational Readings with Simulated Learners
Ryan T. Woo, Anmol Rao, Aryan Keluskar +1
We present a framework for evaluating adaptive personalization of educational reading materials with theory-grounded simulated learners. The system builds a learning-objective and…
Tri-Accel: Curvature-Aware Precision-Adaptive and Memory-Elastic Optimization for Efficient GPU Usage
Mohsen Sheibanian, Pouya Shaeri, Alimohammad Beigi +2
Deep neural networks are increasingly bottlenecked by the cost of optimization, both in terms of GPU memory and compute time. Existing acceleration techniques, such as mixed precis…
Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering
Aryan Keluskar, Amrita Bhattacharjee, Huan Liu
Ambiguity in natural language poses significant challenges to Large Language Models (LLMs) used for open-domain question answering. LLMs often struggle with the inherent uncertaint…