Abstract
Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. Unfortunately, the explainability and accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure, are only accessible via rate-limited, geo-locked, and censored web interfaces, and lack publicly available code and technical reports. Moreover, the lack of tooling for understanding the massive datasets used to train and produced by LLMs presents a critical challenge for explainability research. This talk will be an overview of Nomic AI’s efforts to address these challenges through its two core initiatives: GPT4All and Atlas- Anthology ID:
- 2023.nlposs-1.28
- Volume:
- Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Liling Tan, Dmitrijs Milajevs, Geeticka Chauhan, Jeremy Gwinnup, Elijah Rippeth
- Venues:
- NLPOSS | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 247–247
- Language:
- URL:
- https://aclanthology.org/2023.nlposs-1.28
- DOI:
- 10.18653/v1/2023.nlposs-1.28
- Cite (ACL):
- Brandon Duderstadt and Yuvanesh Anand. 2023. Towards Explainable and Accessible AI. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 247–247, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Towards Explainable and Accessible AI (Duderstadt & Anand, NLPOSS-WS 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.nlposs-1.28.pdf