Impact of Synthetic Data Generators on Alignment Robustness in Multimodal Models with Corrupted Tabular-Text Pairs

Abstract

Abstract: Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g., specifying attributes like length and style), which have the potential to yield diverse and attributed generated data. Our inves

Research Question

What is the impact of synthetic data generators on the alignment robustness of multimodal models when processing corrupted tabular-text pairs?

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