[Corpora-List] [CfP] Challenges & Perspectives in Creating Large Language Models

Matthias Gallé mgalle at gmail.com
Wed Jan 26 17:36:25 CET 2022


*Call for Papers: Workshop on Challenges & Perspectives in Creating Large Language Models* May 27th 2022 (w/ ACL) https://bigscience.huggingface.co/acl-2022

Two years after the appearance of GPT-3, large language models seem to have taken over NLP. Their capabilities, limitations, societal impact and the potential new applications they unlocked have been discussed and debated at length. A handful of replication studies have been published since then, confirming some of the initial findings and discovering new limitations. This workshop aims to gather researchers and practitioners involved in the creation of these models in order to:

1. Share ideas on the next directions of research in this field, including – but not limited to – grounding, multi-modal models, continuous updates and reasoning capabilities. 2. Share best-practices, brainstorm solutions to identified limitations and discuss challenges, such as:

- ‍*Infrastructure*. What are the infrastructure and software challenges

involved in scaling models to billions or trillions of parameters, and

deploying training and inference on distributed servers when each model

replicas is itself larger than a single node capacity?

- ‍*Data*. While the self-supervised setting dispenses with human

annotation, the importance of cleaning, filtering and the bias and

limitation in existing or reported corpora has become more and more

apparent over the last years.

- ‍*Ethical & Legal frameworks*. What type of data can/should be used,

what type of access should be provided, what filters are or should be

necessary?

- ‍*Evaluation*. Investigating the diversity of intrinsic and extrinsic

evaluation measures, how do they correlate and how the performances of a

very large pretrained language model should be evaluated.

- *‍Training efficiency.* Discussing the practical scaling approaches,

practical questions around large scale training hyper-parameters and

early-stopping conditions. Discussing measures to reduce the associated

energy consumption.

This workshop is organized by the BigScience <https://bigscience.huggingface.co/> initiative and will also serve as the closing session of this one year-long initiative aimed at developing a multilingual large language model, which is currently gathering 900 researchers from more than 60 countries and 250 institutions. Its goal is to investigate the creation of a large scale dataset and model from a very wide diversity of angles.

*Submissions* We call for relevant contributions, either in long (8 pages) or short (4 pages) format. Accepted papers will be presented during a poster session. Submissions can be archival or non-archival. Submission opens on February 1st, 2022 and should be made via OpenReview ( https://openreview.net/group?id=aclweb.org/ACL/2022/Workshop/BigScience).

*Dates* Feb. 28, 2022: Submission Deadline ‍March 26, 2022: Notification of Acceptance ‍April 10, 2022: Camera-ready papers due -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 3378 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20220126/87b4c499/attachment.txt>



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