Counterfactual Augmentation in LLM Reasoning Under Temporal Shift

Abstract

Abstract: We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models, which achieve the state-of-the-art privacy versus utility tradeoffs on many standard NLP tasks. We propose a meta-framework for this problem, inspired by the recent success of highly parameter-efficient methods for fine-tuning. Our experiments show that differentially private adaptations of these approaches outperform previous private algorithms in three important dimensions: utility, privacy, and the computational and memory cost of private training. On many commo

Research Question

How does counterfactual data augmentation affect LLM reasoning accuracy on the BigBench Hard suite under temporal distribution shift compared to standard fine-tuning?

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