Liesenfeld, A., Lopez, A. & Dingemanse, M. 2023. βOpening up ChatGPT: Tracking Openness, Transparency, and Accountability in Instruction-Tuned Text Generators.β In CUI '23: Proceedings of the 5th International Conference on Conversational User Interfaces. July 19-21, Eindhoven. doi: 10.1145/3571884.3604316 (arxiv).
There is a growing amount of instruction-tuned text generators billing themselves as 'open source'. How open are they really? paper repo
How to use this table. Every cell records a three-level openness judgement (βοΈ open, ~ partial or β closed) with a direct link to the available evidence; on hover, the cell will display the notes we have on file for that judgement. At the end of a row, the Β§ is a direct link to source data. The table is sorted by cumulative openness, where βοΈ is 1, ~ is 0.5 and β is 0 points.
Why is openness important?
Open research is the lifeblood of cumulative progress in science and engineering. Openness is key for fundamental research, for fostering critical computational literacy, and for making informed choices for or against deployment of LLM+RLHF architectures. The closed & proprietary nature of ChatGPT and kin makes them fundamentally unfit for responsible use in research and education.
Open alternatives provide ways to build reproducible workflows, chart resource costs, and lessen reliance on corporate whims. One aim of our work here is to provide tools to track openness, transparency and accountability in the fast-evolving landscape of instruction-tuned text generators. Read more in the paper or contribute to the repo.
If you know a model that should be listed here or a data point that needs updating, please see guidelines for contributors. We welcome any contribution, whether it's a quick addition to our awesomelist or a more detail-oriented contribution to the metadata for a specific project.
TL;DR
Our paper makes the following contributions:
- We review the risks of relying on proprietary software
- We review best practices for open, transparent and accountable 'AI'
- We find over 20 ChatGPT alternatives at varying degrees of openness, development and documentation
- We argue that tech is never a fait accompli unless we make it so, and that openness enables critical computational literacy
We find the following recurrent patterns:
- Many projects inherit data of dubious legality
- Few projects share the all-important instruction-tuning
- Preprints are rare, peer-reviewed papers even rarer
- Synthetic instruction-tuning data is on the rise, with unknown consequences that are in need of research
We conclude as follows:
Openness is not the full solution to the scientific and ethical challenges of conversational text generators. Open data will not mitigate the harmful consequences of thoughtless deployment of large language models, nor the questionable copyright implications of scraping all publicly available data from the internet. However, openness does make original research possible, including efforts to build reproducible workflows and understand the fundamentals of LLM + RLHF architectures. Openness also enables checks and balances, fostering a culture of accountability for data and its curation, and for models and their deployment. We hope that our work provides a small step in this direction.
Liesenfeld, Andreas, Alianda Lopez, and Mark Dingemanse. 2023. βOpening up ChatGPT: Tracking Openness, Transparency, and Accountability in Instruction-Tuned Text Generators.β In CUI '23: Proceedings of the 5th International Conference on Conversational User Interfaces. July 19-21, Eindhoven. doi: 10.1145/3571884.3604316 (arxiv).