ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917655198302208 |
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| author | Yang, Jackie Junrui Shi, Yingtian Zhang, Yuhan Li, Karina Rosli, Daniel Wan Jain, Anisha Zhang, Shuning Li, Tianshi Landay, James A. Lam, Monica S. |
| author_facet | Yang, Jackie Junrui Shi, Yingtian Zhang, Yuhan Li, Karina Rosli, Daniel Wan Jain, Anisha Zhang, Shuning Li, Tianshi Landay, James A. Lam, Monica S. |
| contents | By combining voice and touch interactions, multimodal interfaces can surpass the efficiency of either modality alone. Traditional multimodal frameworks require laborious developer work to support rich multimodal commands where the user's multimodal command involves possibly exponential combinations of actions/function invocations. This paper presents ReactGenie, a programming framework that better separates multimodal input from the computational model to enable developers to create efficient and capable multimodal interfaces with ease. ReactGenie translates multimodal user commands into NLPL (Natural Language Programming Language), a programming language we created, using a neural semantic parser based on large-language models. The ReactGenie runtime interprets the parsed NLPL and composes primitives in the computational model to implement complex user commands. As a result, ReactGenie allows easy implementation and unprecedented richness in commands for end-users of multimodal apps. Our evaluation showed that 12 developers can learn and build a nontrivial ReactGenie application in under 2.5 hours on average. In addition, compared with a traditional GUI, end-users can complete tasks faster and with less task load using ReactGenie apps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_09649 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Models Yang, Jackie Junrui Shi, Yingtian Zhang, Yuhan Li, Karina Rosli, Daniel Wan Jain, Anisha Zhang, Shuning Li, Tianshi Landay, James A. Lam, Monica S. Human-Computer Interaction Computation and Language By combining voice and touch interactions, multimodal interfaces can surpass the efficiency of either modality alone. Traditional multimodal frameworks require laborious developer work to support rich multimodal commands where the user's multimodal command involves possibly exponential combinations of actions/function invocations. This paper presents ReactGenie, a programming framework that better separates multimodal input from the computational model to enable developers to create efficient and capable multimodal interfaces with ease. ReactGenie translates multimodal user commands into NLPL (Natural Language Programming Language), a programming language we created, using a neural semantic parser based on large-language models. The ReactGenie runtime interprets the parsed NLPL and composes primitives in the computational model to implement complex user commands. As a result, ReactGenie allows easy implementation and unprecedented richness in commands for end-users of multimodal apps. Our evaluation showed that 12 developers can learn and build a nontrivial ReactGenie application in under 2.5 hours on average. In addition, compared with a traditional GUI, end-users can complete tasks faster and with less task load using ReactGenie apps. |
| title | ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Models |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2306.09649 |