INTELMO: Enhancing Models’ Adoption of Interactive Interfaces
Chunxu Yang, Chien-Sheng Wu, Lidiya Murakhovs’ka, Philippe Laban, Xiang Chen
Abstract
This paper presents INTELMO, an easy-to-use library to help model developers adopt user-faced interactive interfaces and articles from real-time RSS sources for their language models. The library categorizes common NLP tasks and provides default style patterns, streamlining the process of creating interfaces with minimal code modifications while ensuring an intuitive user experience. Moreover, INTELMO employs a multi-granular hierarchical abstraction to provide developers with fine-grained and flexible control over user interfaces. INTELMO is under active development, with document available at https://intelmo.github.io.- Anthology ID:
- 2023.emnlp-demo.14
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Yansong Feng, Els Lefever
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 161–166
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-demo.14
- DOI:
- 10.18653/v1/2023.emnlp-demo.14
- Cite (ACL):
- Chunxu Yang, Chien-Sheng Wu, Lidiya Murakhovs’ka, Philippe Laban, and Xiang Chen. 2023. INTELMO: Enhancing Models’ Adoption of Interactive Interfaces. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 161–166, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- INTELMO: Enhancing Models’ Adoption of Interactive Interfaces (Yang et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.emnlp-demo.14.pdf