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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…