The language of prompting: What linguistic properties make a prompt successful?

• URL: http://arxiv.org/abs/2311.01967v1
• Date: Fri, 3 Nov 2023 15:03:36 GMT
• Title: The language of prompting: What linguistic properties make a prompt successful?
• Authors: Alina Leidinger, Robert van Rooij, Ekaterina Shutova
• Abstract summary: LLMs can be prompted to achieve impressive zero-shot or few-shot performance in many NLP tasks. Yet, we still lack a systematic understanding of how linguistic properties of prompts correlate with task performance. We investigate both grammatical properties such as mood, tense, aspect and modality, as well as lexico-semantic variation through the use of synonyms.
• Score: 13.034603322224548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The latest generation of LLMs can be prompted to achieve impressive zero-shot or few-shot performance in many NLP tasks. However, since performance is highly sensitive to the choice of prompts, considerable effort has been devoted to crowd-sourcing prompts or designing methods for prompt optimisation. Yet, we still lack a systematic understanding of how linguistic properties of prompts correlate with task performance. In this work, we investigate how LLMs of different sizes, pre-trained and instruction-tuned, perform on prompts that are semantically equivalent, but vary in linguistic structure. We investigate both grammatical properties such as mood, tense, aspect and modality, as well as lexico-semantic variation through the use of synonyms. Our findings contradict the common assumption that LLMs achieve optimal performance on lower perplexity prompts that reflect language use in pretraining or instruction-tuning data. Prompts transfer poorly between datasets or models, and performance cannot generally be explained by perplexity, word frequency, ambiguity or prompt length. Based on our results, we put forward a proposal for a more robust and comprehensive evaluation standard for prompting research.

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