DONOD Pruning vs. Random Pruning and Full Fine-Tuning on LLaMA-2-7B Cross-Domain Benchmarks

Abstract

Abstract: Recent work by Zellers et al. (2018) introduced a new task of commonsense natural language inference: given an event description such as "A woman sits at a piano," a machine must select the most likely followup: "She sets her fingers on the keys." With the introduction of BERT, near human-level performance was reached. Does this mean that machines can perform human level commonsense inference? In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset. Though its questions are trivial for humans

Research Question

How does the DONOD pruning method compare to random data pruning and full-dataset fine-tuning on LLaMA-2-7B for cross-domain held-out benchmarks (MMLU, HellaSwag, WinoGrande) in terms of accuracy retention?

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