What is the impact of negative sampling versus domain-specific fine-tuning on exact match and F1 scores across

Abstract

Abstract: Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models' pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording.

Research Question

What is the impact of negative sampling versus domain-specific fine-tuning on exact match and F1 scores across the 12 in-domain and out-of-domain datasets in the MRQA 2019 benchmark?

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