Understanding How Model Size Affects Few-shot Instruction Prompting

• URL: http://arxiv.org/abs/2212.01907v1
• Date: Sun, 4 Dec 2022 19:59:52 GMT
• Title: Understanding How Model Size Affects Few-shot Instruction Prompting
• Authors: Ayrton San Joaquin and Ardy Haroen
• Abstract summary: We investigate how the model size affects the model's ability to discriminate a word's meaning in a given context. We introduce a dataset called DeltaWords, which evaluates a model's ability to follow instructions. We show a weak inverse scaling trend, where task accuracy degrades as model size increase.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models are affected by the phenomena of memorizing and forgetting their training data. But how do these vary by model size? We work towards this question by investigating how the model size affects the model's ability to discriminate a word's meaning in a given context. We introduce a dataset called DeltaWords, which evaluates a model's ability to follow instructions to select a sentence which replaces the target word with its antonym. We show a weak inverse scaling trend, where task accuracy degrades as model size increase, under extremely few-shot prompting regimes. We show that increasing the number of examples tend to disproportionately benefit larger models than smaller models.

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