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cs.CL2025
Assessing Robustness to Spurious Correlations in Post-Training Language Models
Julia Shuieh, Prasann Singhal, Apaar Shanker +3
Supervised and preference-based fine-tuning techniques have become popular for aligning large language models (LLMs) with user intent and correctness criteria. However, real-world…
cs.CL2024
Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs
Yung-Chieh Chan, George Pu, Apaar Shanker +4
As large language models (LLMs) are applied to more use cases, creating high quality, task-specific datasets for fine-tuning becomes a bottleneck for model improvement. Using high…