1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2026
How Much Do You Trust A Dog? Quantifying Hint-Following in Reasoning Language Models
Anxin Guo, Chirag Pabbaraju, Sara Ahmadian +2
Large language models (LLMs) often exhibit sycophancy, altering their answers to align with user suggestions even on objective factual and reasoning tasks. Yet the quantitative for…
cs.CL2024★ 1 cited
Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement
Simon Yu, Liangyu Chen, Sara Ahmadian +1
Finetuning large language models on instruction data is crucial for enhancing pre-trained knowledge and improving instruction-following capabilities. As instruction datasets prolif…
cs.DS2024
GIST: Greedy Independent Set Thresholding for Max-Min Diversification with Submodular Utility
Matthew Fahrbach, Srikumar Ramalingam, Morteza Zadimoghaddam +3
This work studies a novel subset selection problem called max-min diversification with monotone submodular utility (), which has a wide range of applications in mach…