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cs.CL2025
Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability
Taylor Sorensen, Benjamin Newman, Jared Moore +5
Language model post-training has enhanced instruction-following and performance on many downstream tasks, but also comes with an often-overlooked cost on tasks with many possible v…
cs.CL2025
Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models
Abhilasha Ravichander, Jillian Fisher, Taylor Sorensen +6
High-quality training data has proven crucial for developing performant large language models (LLMs). However, commercial LLM providers disclose few, if any, details about the data…