Very nice my friend. Ping me, I will be creating a substantial amount of synthetic data.
Huu Nguyen
huu-ontocord
AI & ML interests
None yet
Recent Activity
updated
a dataset
about 4 hours ago
ontocord/MixtureVitae-300BT
authored
a paper
4 days ago
EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion
Recognition
authored
a paper
4 days ago
EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech
Emotion Detection
Organizations
reacted to
sted97's
post with 🤗🔥
about 1 year ago
Post
2463
📣 I'm thrilled to announce "ALERT: A Comprehensive #Benchmark for Assessing #LLMs’ Safety through #RedTeaming" 🚨
📄 Paper: https://arxiv.org/pdf/2404.08676.pdf
🗃️ Repo: https://github.com/Babelscape/ALERT
🤗 ALERT benchmark: Babelscape/ALERT
🤗 ALERT DPO data: Babelscape/ALERT_DPO
As a key design principle for ALERT, we developed a fine-grained safety risk taxonomy (Fig. 2). This taxonomy serves as the foundation for the benchmark to provide detailed insights about a model’s weaknesses and vulnerabilities as well as inform targeted safety enhancements 🛡️
For collecting our prompts, we started from the popular
Anthropic's HH-RLHF data, and used automated strategies to filter/classify prompts. We then designed templates to create new prompts (providing sufficient support for each category, cf. Fig. 3) and implemented adversarial attacks.
In our experiments, we extensively evaluated several open- and closed-source LLMs (e.g. #ChatGPT, #Llama and #Mistral), highlighting their strengths and weaknesses (Table 1).
For more details, check out our preprint: https://arxiv.org/pdf/2404.08676.pdf 🤓
Huge thanks to @felfri , @PSaiml , Kristian Kersting, @navigli , @huu-ontocord and @BoLi-aisecure (and all the organizations involved: Babelscape, Sapienza NLP, TU Darmstadt, Hessian.AI, DFKI, Ontocord.AI, UChicago and UIUC)🫂
📄 Paper: https://arxiv.org/pdf/2404.08676.pdf
🗃️ Repo: https://github.com/Babelscape/ALERT
🤗 ALERT benchmark: Babelscape/ALERT
🤗 ALERT DPO data: Babelscape/ALERT_DPO
As a key design principle for ALERT, we developed a fine-grained safety risk taxonomy (Fig. 2). This taxonomy serves as the foundation for the benchmark to provide detailed insights about a model’s weaknesses and vulnerabilities as well as inform targeted safety enhancements 🛡️
For collecting our prompts, we started from the popular
Anthropic's HH-RLHF data, and used automated strategies to filter/classify prompts. We then designed templates to create new prompts (providing sufficient support for each category, cf. Fig. 3) and implemented adversarial attacks.
In our experiments, we extensively evaluated several open- and closed-source LLMs (e.g. #ChatGPT, #Llama and #Mistral), highlighting their strengths and weaknesses (Table 1).
For more details, check out our preprint: https://arxiv.org/pdf/2404.08676.pdf 🤓
Huge thanks to @felfri , @PSaiml , Kristian Kersting, @navigli , @huu-ontocord and @BoLi-aisecure (and all the organizations involved: Babelscape, Sapienza NLP, TU Darmstadt, Hessian.AI, DFKI, Ontocord.AI, UChicago and UIUC)🫂
- 1 reply
reacted to
akhaliq's
post with ❤️
about 1 year ago
Post
2232
Aurora-M
The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order (2404.00399)
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community development. However, such existing models face challenges: limited multilingual capabilities, continual pretraining causing catastrophic forgetting, whereas pretraining from scratch is computationally expensive, and compliance with AI safety and development laws. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Aurora-M is rigorously evaluated across various tasks and languages, demonstrating robustness against catastrophic forgetting and outperforming alternatives in multilingual settings, particularly in safety evaluations.
The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order (2404.00399)
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community development. However, such existing models face challenges: limited multilingual capabilities, continual pretraining causing catastrophic forgetting, whereas pretraining from scratch is computationally expensive, and compliance with AI safety and development laws. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Aurora-M is rigorously evaluated across various tasks and languages, demonstrating robustness against catastrophic forgetting and outperforming alternatives in multilingual settings, particularly in safety evaluations.
posted
an
update
about 1 year ago
Post
1735
We would like to announce our Aurora-M multilingual models which is based on Starcoderplus.
Twitter: https://twitter.com/ontocord/status/1772778544051155029
LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7178521998845759488/
Blog post: https://huggingface.co/blog/mayank-mishra/aurora
Arxiv: Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order (2404.00399)
Current LLMs are very susceptible to generating toxic, harmful and even dangerous content. They can also generate outputs with gender or racial biases. The Biden-Harris Executive Order https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence) sets forth guidelines on what is considered a safe AI system.
Following up on these guidelines, we present the world's first open source Biden-Harris Executive Order Red teamed Multilingual Language Model: Aurora-M. Inspired by BigScience, the model is trained on 5 languages: English, Hindi, Japanese, Vietnamese and Finnish.
* Red teamed model: aurora-m/aurora-m-biden-harris-redteamed tuned according to the order mentioned above)
* Base model: aurora-m/aurora-m-base (not safety tuned)
* Instruct model: aurora-m/aurora-m-instruct (not safety tuned)
@mayank-mishra @cabbage972 @sted97 @Xa9aX @Taishi-N324 @Muennighoff @vumichien @prateeky2806 @felfri @spyysalo and many many others!
Twitter: https://twitter.com/ontocord/status/1772778544051155029
LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7178521998845759488/
Blog post: https://huggingface.co/blog/mayank-mishra/aurora
Arxiv: Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order (2404.00399)
Current LLMs are very susceptible to generating toxic, harmful and even dangerous content. They can also generate outputs with gender or racial biases. The Biden-Harris Executive Order https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence) sets forth guidelines on what is considered a safe AI system.
Following up on these guidelines, we present the world's first open source Biden-Harris Executive Order Red teamed Multilingual Language Model: Aurora-M. Inspired by BigScience, the model is trained on 5 languages: English, Hindi, Japanese, Vietnamese and Finnish.
* Red teamed model: aurora-m/aurora-m-biden-harris-redteamed tuned according to the order mentioned above)
* Base model: aurora-m/aurora-m-base (not safety tuned)
* Instruct model: aurora-m/aurora-m-instruct (not safety tuned)
@mayank-mishra @cabbage972 @sted97 @Xa9aX @Taishi-N324 @Muennighoff @vumichien @prateeky2806 @felfri @spyysalo and many many others!