How does back-translation paraphrasing affect the robustness of LLM question answering performance across diff

Abstract

Abstract: NLP practitioners often want to take existing trained models and apply them to data from new domains. While fine-tuning or few-shot learning can be used to adapt a base model, there is no single recipe for making these techniques work; moreover, one may not have access to the original model weights if it is deployed as a black box. We study how to improve a black box model's performance on a new domain by leveraging explanations of the model's behavior. Our approach first extracts a set of features combining human intuition about the task with model attributions generated by black box interpre

Research Question

How does back-translation paraphrasing affect the robustness of LLM question answering performance across different domains when evaluated on the MRQA and MultiQA datasets?

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