How does negative sampling performance scale across different LLM architectures (7B vs 70B) when evaluated on

Abstract

Abstract: To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation. We find a simple negative sampling technique to be particularly effective, even though it is typically used for datasets that include unanswerable questions, such as SQuAD 2.0. When applied in conjunction with per-domain sampling, our XLNet (Yang et al., 2019)-based submission achieved the second

Research Question

How does negative sampling performance scale across different LLM architectures (7B vs 70B) when evaluated on out-of-distribution benchmarks like MRQA 2019, and what is the optimal balance between negative sampling ratio and model scale for domain-agnostic QA performance?

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