CFP of NLP-OSS @ EMNLP 2023 ((apologies for cross-posting) ---------------------------------------------------------------- *Workshop for NLP Open Source Software (NLP-OSS)* 06 Dec 2023, Co-located with EMNLP 2023 (https://nlposs.github.io/)

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(apologies for cross-posting)

Workshop for NLP Open Source Software (NLP-OSS)

06 Dec 2023, Co-located with EMNLP 2023

Deadline for Long and Short Paper submission: 09 August, 2023
(23:59, GMT-11)

You have tried to use the latest, bestest, fastest LLM models and bore grievances but found the solution after hours of coffee and computer staring. Share that at NLP-OSS and suggest how open source could change for the better (e.g. best practices, documentation, API design etc.)

You came across an awesome SOTA system on NLP task X and no LLM has beaten its F1 score. However, the code is now stale and it takes a dinosaur to understand the code. Share your experience at NLP-OSS and propose how to “replicate” these forgotten systems.

You see this shiny GPT from a blog post, tried it to reproduce similar results on a different task and it just doesn’t work on your dataset. You did some magic to the code and now it works. Show us how you did it! Though they’re small tweaks, well-motivated and empirically tested are valid submissions to NLP-OSS.

You have tried 101 NLP tools and there’s none that really do what you want. So you wrote your own shiny new package and made it open source. Tell us why your package is better than the existing tools. How did you design the code? Is it going to be a one-time thing? Or would you like to see thousands of people using it?

You have heard enough of open-source LLM and pseudo-open-source GPT but not enough about how it can be used for your use-case or your commercial product at scale. So you contacted your legal department and they explained to you about how data, model and code licenses work. Sharing the knowledge with the NLP-OSS community.

You have a position/opinion to share about free vs open vs closed source LLMs and have valid arguments, references or survey/data to support your position. We would want to hear more about it.

At last, you’ve found the avenue to air these issues in an academic platform at the NLP-OSS workshop!!!

Sharing your experiences, suggestions and analysis from/of NLP-OSS

P/S:

1st CALL FOR PAPERS

Workshop for NLP Open Source Software (NLP-OSS)

06 Dec 2023, Co-located with EMNLP 2023

Deadline for Long and Short Paper submission: 09 August, 2023
(23:59, GMT-11)

The Third Workshop for NLP Open Source Software (NLP-OSS) will be co-located
with EMNLP 2023 on 06 Dec 2023.

Focusing more on the social and engineering aspect of NLP software
and less on scientific novelty or state-of-art models, the Workshop for NLP-OSS
is an academic forum to advance open source developments for NLP research,
teaching and application.

NLP-OSS also provides an academic workshop to announce new software/features,
promote the collaborative culture and best practices that go beyond the conferences.

We invite full papers (8 pages) or short papers (4 pages) on topics related to
NLP-OSS broadly categorized into (i) software development, (ii) scientific
contribution and (iii) NLP-OSS case studies.

  • Software Development

    • Designing and developing NLP-OSS
    • Licensing issues in NLP-OSS
    • Backwards compatibility and stale code in NLP-OSS
    • Growing, maintaining and motivating an NLP-OSS community
    • Best practices for NLP-OSS documentation and testing
    • Contribution to NLP-OSS without coding
    • Incentivizing OSS contributions in NLP
    • Commercialization and Intellectual Property of NLP-OSS
    • Defining and managing NLP-OSS project scope
    • Issues in API design for NLP
    • NLP-OSS software interoperability
    • Analysis of the NLP-OSS community

  • Scientific Contribution

    • Surveying OSS for specific NLP task(s)
    • Demonstration, introductions and/or tutorial of NLP-OSS
    • Small but useful NLP-OSS
    • NLP components in ML OSS
    • Citations and references for NLP-OSS
    • OSS and experiment replicability
    • Gaps between existing NLP-OSS
    • Task-generic vs task-specific software

  • Case studies

    • Case studies of how a specific bug is fixed or feature is added
    • Writing wrappers for other NLP-OSS
    • Writing open-source APIs for open data
    • Teaching NLP with OSS
    • NLP-OSS in the industry

Submission should be formatted according to the EMNLP 2023 templates and submitted to OpenReview

ORGANIZERS

Geeticka Chauhan, Massachusetts Institute of Technology
Dmitrijs Milajevs, Grayscale AI
Elijah Rippeth, University of Maryland
Jeremy Gwinnup, Air Force Research Laboratory
Liling Tan, Amazon

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