Computer Science > Computation and Language
arXiv:2208.07852 (cs)
[Submitted on 16 Aug 2022]
Title:Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models
Authors:Hendrik Strobelt,Albert Webson,Victor Sanh,Benjamin Hoover,Johanna Beyer,Hanspeter Pfister,Alexander M. Rush
View a PDF of the paper titled Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models, by Hendrik Strobelt and 6 other authors
View PDFAbstract:State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training. This approach has gained popularity in recent years, and researchers have demonstrated prompts that achieve strong accuracy on specific NLP tasks. However, finding a prompt for new tasks requires experimentation. Different prompt templates with different wording choices lead to significant accuracy differences. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts. We developed a workflow that allows users to first focus on model feedback using small data before moving on to a large data regime that allows empirical grounding of promising prompts using quantitative measures of the task. The tool then allows easy deployment of the newly created ad-hoc models. We demonstrate the utility of PromptIDE (demo atthis http URL) and our workflow using several real-world use cases.
| Comments: | 9 pages content, 2 pages references |
| Subjects: | Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2208.07852 [cs.CL] |
| (orarXiv:2208.07852v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2208.07852 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models, by Hendrik Strobelt and 6 other authors
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