Abstract
Current approaches to testing and debugging NLP models rely on highly variable human creativity and extensive labor, or only work for a very restrictive class of bugs. We present AdaTest, a process which uses large scale language models (LMs) in partnership with human feedback to automatically write unit tests highlighting bugs in a target model. Such bugs are then addressed through an iterative text-fix-retest loop, inspired by traditional software development. In experiments with expert and non-expert users and commercial / research models for 8 different tasks, AdaTest makes users 5-10x more effective at finding bugs than current approaches, and helps users effectively fix bugs without adding new bugs.- Anthology ID:
- 2022.acl-long.230
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3253–3267
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.230
- DOI:
- 10.18653/v1/2022.acl-long.230
- Cite (ACL):
- Marco Tulio Ribeiro and Scott Lundberg. 2022. Adaptive Testing and Debugging of NLP Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3253–3267, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Adaptive Testing and Debugging of NLP Models (Ribeiro & Lundberg, ACL 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.acl-long.230.pdf
- Data
- GLUE, PAWS