Opening up ChatGPT⚡FAccT'24 paper⚡ Liesenfeld, Andreas, and Mark Dingemanse. 2024. ‘Rethinking Open Source Generative AI: Open-Washing and the EU AI Act’. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24). Rio de Janeiro, Brazil: ACM. (PDF).
There is a growing amount of instruction-tuned text generators billing themselves as 'open source'. How open are they really? FAccT'24 CUI'23 repo
| Project | Availability | Documentation | Access | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (maker, bases, URL) | Open code | LLM data | LLM weights | RL data | RL weights | License | Code | Architecture | Preprint | Paper | Modelcard | Datasheet | Package | API |
| OLMo 7B Instruct | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✘ | ✔︎ | ✔︎ | ✔︎ | ~ |
| Ai2 | LLM base: OLMo 7B | RL base: OpenInstruct | 12.5 | |||||||||||
| BLOOMZ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ~ | ~ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✘ | ✔︎ |
| bigscience-workshop | LLM base: BLOOMZ, mT0 | RL base: xP3 | 12.0 | |||||||||||
| AmberChat | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ~ | ~ | ✔︎ | ✘ | ~ | ~ | ✘ | ✔︎ |
| LLM360 | LLM base: Amber | RL base: ShareGPT + Evol-Instruct (synthetic) | 10.0 | |||||||||||
| Open Assistant | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✘ | ✔︎ | ✔︎ | ✔︎ | ~ | ✘ | ✘ | ✘ | ✔︎ | ✔︎ |
| LAION-AI | LLM base: Pythia 12B | RL base: OpenAssistant Conversations | 9.5 | |||||||||||
| OpenChat 3.5 7B | ✔︎ | ✘ | ✔︎ | ✘ | ✔︎ | ✔︎ | ~ | ✔︎ | ✔︎ | ✔︎ | ~ | ✘ | ✔︎ | ~ |
| Tshinghua University | LLM base: Mistral 7B | RL base: ShareGPT with C-RLFT | 9.5 | |||||||||||
| Pythia-Chat-Base-7B-v0.16 | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✘ | ✔︎ | ✔︎ | ✔︎ | ~ | ✘ | ~ | ~ | ✔︎ | ✘ |
| togethercomputer | LLM base: EleutherAI pythia | RL base: OIG | 9.5 | |||||||||||
| Cerebras GPT 111M Instruction | ~ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ~ | ✘ | ✔︎ | ~ | ✘ | ✘ | ✔︎ | ✘ | ✔︎ |
| Cerebras + Schramm | LLM base: Cerebras | RL base: Alpaca (synthetic) | 8.5 | |||||||||||
| RedPajama-INCITE-Instruct-7B | ~ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ~ | ~ | ~ | ✘ | ✘ | ✔︎ | ✔︎ | ✘ | ~ |
| TogetherComputer | LLM base: RedPajama-INCITE-7B-Base | RL base: various (GPT-JT recipe) | 8.5 | |||||||||||
| dolly | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ✘ | ✔︎ | ✔︎ | ✔︎ | ~ | ✘ | ✘ | ✘ | ✔︎ | ✘ |
| databricks | LLM base: EleutherAI pythia | RL base: databricks-dolly-15k | 8.5 | |||||||||||
| Tulu V2 DPO 70B | ✔︎ | ✘ | ~ | ✔︎ | ✔︎ | ~ | ~ | ~ | ✔︎ | ✘ | ~ | ~ | ✘ | ✔︎ |
| AllenAI | LLM base: Llama2 | RL base: Tulu SFT, Ultrafeedback | 8.0 | |||||||||||
| MPT-30B Instruct | ✔︎ | ~ | ✔︎ | ~ | ✘ | ✔︎ | ✔︎ | ~ | ✘ | ✘ | ~ | ✘ | ✔︎ | ~ |
| MosaicML | LLM base: MosaicML | RL base: dolly, anthropic | 7.5 | |||||||||||
| MPT-7B Instruct | ✔︎ | ~ | ✔︎ | ~ | ✘ | ✔︎ | ✔︎ | ~ | ✘ | ✘ | ✔︎ | ✘ | ✔︎ | ✘ |
| MosaicML | LLM base: MosaicML | RL base: dolly, anthropic | 7.5 | |||||||||||
| trlx | ✔︎ | ✔︎ | ✔︎ | ~ | ✘ | ✔︎ | ✔︎ | ~ | ✘ | ✘ | ✘ | ✘ | ~ | ✔︎ |
| carperai | LLM base: various (pythia, flan, OPT) | RL base: various | 7.5 | |||||||||||
| NeuralChat 7B | ~ | ✘ | ✔︎ | ✔︎ | ✔︎ | ✔︎ | ~ | ~ | ✘ | ✘ | ~ | ~ | ~ | ✘ |
| Intel | LLM base: Mistral 7B | RL base: Orca | 7.0 | |||||||||||
| Vicuna 13B v 1.3 | ✔︎ | ~ | ✔︎ | ✘ | ✘ | ~ | ✔︎ | ✘ | ✔︎ | ✘ | ~ | ✘ | ✔︎ | ~ |
| LMSYS | LLM base: LLaMA | RL base: ShareGPT | 7.0 | |||||||||||
| minChatGPT | ✔︎ | ✔︎ | ✔︎ | ~ | ✘ | ✔︎ | ✔︎ | ~ | ✘ | ✘ | ✘ | ✘ | ✘ | ✔︎ |
| ethanyanjiali | LLM base: GPT2 | RL base: anthropic | 7.0 | |||||||||||
| ChatRWKV | ✔︎ | ~ | ✔︎ | ✘ | ✘ | ✔︎ | ~ | ~ | ~ | ✘ | ✘ | ✘ | ✔︎ | ~ |
| BlinkDL/RWKV | LLM base: RWKV-LM | RL base: alpaca, shareGPT (synthetic) | 6.5 | |||||||||||
| BELLE | ✔︎ | ~ | ~ | ~ | ~ | ✘ | ~ | ✔︎ | ✔︎ | ✘ | ✘ | ~ | ✘ | ✘ |
| KE Technologies | LLM base: LLaMA & BLOOMZ | RL base: alpaca, shareGPT, Belle (synthetic) | 6.0 | |||||||||||
| Geitje Ultra 7B | ✘ | ~ | ✔︎ | ✔︎ | ✔︎ | ✘ | ✘ | ~ | ~ | ✘ | ~ | ~ | ✘ | ~ |
| Bram van Roy | LLM base: Mistral 7B | RL base: Ultrafeedback Dutch (synthetic) | 6.0 | |||||||||||
| Phi 3 Instruct | ✘ | ✘ | ✘ | ✘ | ✔︎ | ✔︎ | ✘ | ✔︎ | ~ | ✘ | ✔︎ | ✘ | ~ | ✔︎ |
| Microsoft | LLM base: Phi3 | RL base: Unspecified | 6.0 | |||||||||||
| WizardLM 13B v1.2 | ~ | ✘ | ~ | ✔︎ | ✔︎ | ~ | ~ | ✔︎ | ✔︎ | ✘ | ✘ | ✘ | ✘ | ✘ |
| Microsoft & Peking University | LLM base: LLaMA2-13B | RL base: Evol-Instruct (synthetic) | 6.0 | |||||||||||
| Airoboros L2 70B GPT4 | ~ | ✘ | ~ | ✔︎ | ✔︎ | ~ | ~ | ~ | ✘ | ✘ | ~ | ~ | ✘ | ✘ |
| Jon Durbin | LLM base: Llama2 | RL base: Airoboros (synthetic) | 5.5 | |||||||||||
| ChatGLM-6B | ~ | ~ | ✔︎ | ✘ | ✘ | ✔︎ | ~ | ~ | ✘ | ~ | ✘ | ✘ | ✘ | ✔︎ |
| THUDM | LLM base: GLM (own) | RL base: Unspecified | 5.5 | |||||||||||
| Mistral 7B-Instruct | ~ | ✘ | ✔︎ | ✘ | ~ | ✔︎ | ✘ | ~ | ~ | ✘ | ✘ | ✘ | ~ | ✔︎ |
| Mistral AI | LLM base: unclear | RL base: unspecified | 5.5 | |||||||||||
| WizardLM-7B | ~ | ~ | ✘ | ✔︎ | ~ | ~ | ~ | ✔︎ | ✔︎ | ✘ | ✘ | ✘ | ✘ | ✘ |
| Microsoft & Peking University | LLM base: LLaMA-7B | RL base: Evol-Instruct (synthetic) | 5.5 | |||||||||||
| Mistral NeMo Instruct | ~ | ✘ | ✔︎ | ✘ | ~ | ✔︎ | ✘ | ~ | ✘ | ✘ | ✘ | ✘ | ~ | ✔︎ |
| Mistral AI | LLM base: Mistral NeMo | RL base: unspecified | 5.0 | |||||||||||
| Qwen 1.5 | ~ | ✘ | ✔︎ | ✘ | ✔︎ | ✘ | ~ | ~ | ✘ | ✘ | ✘ | ✘ | ~ | ✔︎ |
| Alibaba Cloud | LLM base: QwenLM | RL base: Unspecified | 5.0 | |||||||||||
| StableVicuna-13B | ~ | ✘ | ~ | ~ | ~ | ~ | ~ | ~ | ~ | ✘ | ~ | ✘ | ✘ | ~ |
| CarperAI | LLM base: LLaMA | RL base: OASST1 (human), GPT4All (human), Alpaca (synthetic) | 5.0 | |||||||||||
| Falcon-40B-instruct | ✘ | ~ | ✔︎ | ~ | ✘ | ✔︎ | ✘ | ~ | ~ | ✘ | ~ | ✘ | ✘ | ✘ |
| Technology Innovation Institute | LLM base: Falcon 40B | RL base: Baize (synthetic) | 4.5 | |||||||||||
| UltraLM | ✘ | ✘ | ~ | ✔︎ | ~ | ✘ | ✘ | ~ | ✔︎ | ✘ | ~ | ~ | ✘ | ✘ |
| OpenBMB | LLM base: LLaMA2 | RL base: UltraFeedback (part synthetic) | 4.5 | |||||||||||
| Yi 34B Chat | ~ | ✘ | ✔︎ | ✘ | ✔︎ | ~ | ✘ | ✘ | ✔︎ | ✘ | ✘ | ✘ | ✘ | ~ |
| 01.AI | LLM base: Yi 34B | RL base: unspecified | 4.5 | |||||||||||
| Koala 13B | ✔︎ | ~ | ~ | ~ | ✘ | ~ | ~ | ~ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ |
| BAIR | LLM base: LLaMA 13B | RL base: HC3, ShareGPT, alpaca (synthetic) | 4.0 | |||||||||||
| Llama 3.1 | ~ | ✘ | ~ | ✘ | ✘ | ✘ | ~ | ~ | ✘ | ✘ | ~ | ✘ | ✔︎ | ~ |
| Facebook Research | LLM base: Meta Llama 3 | RL base: Meta, undocumented | 4.0 | |||||||||||
| Mixtral 8x7B Instruct | ✘ | ✘ | ✔︎ | ✘ | ~ | ✔︎ | ✘ | ~ | ~ | ✘ | ✘ | ✘ | ~ | ✘ |
| Mistral AI | LLM base: Mistral | RL base: Unspecified | 4.0 | |||||||||||
| Stable Beluga 2 | ✘ | ✘ | ~ | ✘ | ✔︎ | ~ | ✘ | ~ | ~ | ✘ | ~ | ✘ | ✘ | ~ |
| Stability AI | LLM base: LLaMA2 | RL base: Orca-style (synthetic) | 4.0 | |||||||||||
| Stanford Alpaca | ✔︎ | ✘ | ~ | ~ | ~ | ✘ | ~ | ✔︎ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ |
| Stanford University CRFM | LLM base: LLaMA | RL base: Self-Instruct (synthetic) | 4.0 | |||||||||||
| Falcon-180B-chat | ✘ | ~ | ~ | ~ | ~ | ✘ | ✘ | ~ | ~ | ✘ | ~ | ✘ | ✘ | ✘ |
| Technology Innovation Institute | LLM base: Falcon 180B | RL base: OpenPlatypus, Ultrachat, Airoboros (synthetic) | 3.5 | |||||||||||
| Gemma 7B Instruct | ~ | ✘ | ~ | ✘ | ~ | ✘ | ✘ | ~ | ~ | ✘ | ✔︎ | ✘ | ✘ | ✘ |
| Google DeepMind | LLM base: Gemma | RL base: Unspecified | 3.5 | |||||||||||
| Orca 2 | ✘ | ✘ | ~ | ✘ | ✔︎ | ✘ | ✘ | ~ | ~ | ✘ | ~ | ✘ | ✘ | ~ |
| Microsoft Research | LLM base: LLaMA2 | RL base: FLAN, Math, undisclosed (synthetic) | 3.5 | |||||||||||
| Command R+ | ✘ | ✘ | ✘ | ✔︎ | ✔︎ | ~ | ✘ | ✘ | ✘ | ✘ | ~ | ✘ | ✘ | ✘ |
| Cohere AI | LLM base: | RL base: Aya Collection | 3.0 | |||||||||||
| LLaMA2 Chat | ✘ | ✘ | ~ | ✘ | ~ | ✘ | ✘ | ~ | ~ | ✘ | ~ | ✘ | ✘ | ~ |
| Facebook Research | LLM base: LLaMA2 | RL base: Meta, StackExchange, Anthropic | 3.0 | |||||||||||
| Nanbeige2-Chat | ✔︎ | ✘ | ✘ | ✘ | ✔︎ | ~ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ~ |
| Nanbeige LLM lab | LLM base: Unknown | RL base: Unknown | 3.0 | |||||||||||
| Llama 3 Instruct | ✘ | ✘ | ~ | ✘ | ~ | ✘ | ✘ | ~ | ✘ | ✘ | ~ | ✘ | ✘ | ~ |
| Facebook Research | LLM base: Meta Llama 3 | RL base: Meta, undocumented | 2.5 | |||||||||||
| Solar 70B | ✘ | ✘ | ~ | ✘ | ~ | ✘ | ✘ | ✘ | ✘ | ✘ | ~ | ✘ | ✘ | ~ |
| Upstage AI | LLM base: LLaMA2 | RL base: Orca-style, Alpaca-style | 2.0 | |||||||||||
| Xwin-LM | ✘ | ✘ | ~ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ~ |
| Xwin-LM | LLM base: LLaMA2 | RL base: unknown | 1.0 | |||||||||||
| ChatGPT | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ✘ | ~ | ✘ | ✘ | ✘ | ✘ | ✘ |
| OpenAI | LLM base: GPT 3.5 | RL base: Instruct-GPT | 0.5 | |||||||||||
How to use this table. Every cell records a three-level openness judgement (✔︎ open, ~ partial or ✘ closed) with a direct link to the available evidence; on hover, the cell will display the notes we have on file for that judgement. The name of each project is a direct link to source data. The table is sorted by cumulative openness, where ✔︎ is 1, ~ is 0.5 and ✘ is 0 points. Note that RL may refer to RLHF or other forms of fine-tuning aimed at fostering instruction-following behaviour.
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 instruction-tuned LLM 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 (PDF) 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 and feel free to open an issue or pull request in our repository.
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 40 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 instruction-tuned LLM 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.
Papers
Liesenfeld, Andreas, Alianda Lopez, and Mark Dingemanse. 2023. “Opening up ChatGPT: Tracking Openness, Transparency, and Accountability in Instruction-Tuned Text Generators.” In CUI '23: Proceedings of the 5th International Conference on Conversational User Interfaces. July 19-21, Eindhoven. doi: 10.1145/3571884.3604316 (PDF).
Andreas Liesenfeld and Mark Dingemanse. 2024. Rethinking open source generative AI: open washing and the EU AI Act. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). Association for Computing Machinery, New York, NY, USA, 1774–1787. doi: 10.1145/3630106.3659005