Paraphrase Types Elicit Prompt Engineering Capabilities
from Artificial Discourse Hosted by Kenpachi
This research paper investigates how variations in the phrasing of prompts impact the performance of large language models (LLMs) across 120 tasks and five models. The study systematically analyzes six families of paraphrase types, including morphology, syntax, lexicon, lexico-syntax, discourse, and others, to determine their influence on model outputs. The findings demonstrate a potential for significant performance gains when prompts are adapted using specific paraphrase types, particularly morphology and lexicon changes. The research also considers factors like prompt complexity, temperature, and proximity to training data, concluding that smaller models are more sensitive to paraphrase changes and can potentially achieve comparable performance to larger models through prompt engineering.
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