@inproceedings{duderstadt-anand-2023-towards,
title = "Towards Explainable and Accessible {AI}",
author = "Duderstadt, Brandon and
Anand, Yuvanesh",
editor = "Tan, Liling and
Milajevs, Dmitrijs and
Chauhan, Geeticka and
Gwinnup, Jeremy and
Rippeth, Elijah",
booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlposs-1.28",
doi = "10.18653/v1/2023.nlposs-1.28",
pages = "247--247",
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",
}
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<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</abstract>
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%0 Conference Proceedings
%T Towards Explainable and Accessible AI
%A Duderstadt, Brandon
%A Anand, Yuvanesh
%Y Tan, Liling
%Y Milajevs, Dmitrijs
%Y Chauhan, Geeticka
%Y Gwinnup, Jeremy
%Y Rippeth, Elijah
%S Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F duderstadt-anand-2023-towards
%X 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
%R 10.18653/v1/2023.nlposs-1.28
%U https://aclanthology.org/2023.nlposs-1.28
%U https://doi.org/10.18653/v1/2023.nlposs-1.28
%P 247-247
Markdown (Informal)
[Towards Explainable and Accessible AI](https://aclanthology.org/2023.nlposs-1.28) (Duderstadt & Anand, NLPOSS-WS 2023)
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.