andrew--r /
frontend-case-studies
💼 A curated list of talks and articles about real world frontend development
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A curated list of safety-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into the safety implications, challenges, and advancements surrounding these powerful models.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
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Welcome to our Awesome-llm-safety repository! 🥰🥰🥰
🔥 News
🧑💻 Our Work
We've curated a collection of the latest 😋, most comprehensive 😎, and most valuable 🤩 resources on large language model safety (llm-safety). But we don't stop there; included are also relevant talks, tutorials, conferences, news, and articles. Our repository is constantly updated to ensure you have the most current information at your fingertips.
If a resource is relevant to multiple subcategories, we place it under each applicable section. For instance, the "Awesome-LLM-Safety" repository will be listed under each subcategory to which it pertains🤩!.
✔️ Perfect for Majority
🧭 How to Use this Guide
💼 How to Contribution
If you have completed an insightful work or carefully compiled conference papers, we would love to add your work to the repository.
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🌱 If you would like more people to read your recent insightful work, please contact me via email. I can offer you a promotional spot here for up to one month.
Let’s start LLM Safety tutorial!
| Date | Link | Publication | Authors |
|---|---|---|---|
| 2024/5/20 | Managing extreme AI risks amid rapid progress | Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atılım Güneş Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca Dragan, Philip Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann | Science |
| Date | Institute | Publication | Paper |
|---|---|---|---|
| 20.10 | Facebook AI Research | arxiv | Recipes for Safety in Open-domain Chatbots |
| 22.03 | OpenAI | NIPS2022 | Training language models to follow instructions with human feedback |
| 23.07 | UC Berkeley | NIPS2023 | Jailbroken: How Does LLM Safety Training Fail? |
| 23.12 | OpenAI | Open AI | Practices for Governing Agentic AI Systems |
| Date | Type | Title | URL |
|---|---|---|---|
| 22.02 | Toxicity Detection API | Perspective API | linkpaper |
| 23.07 | Repository | Awesome LLM Security | link |
| 23.10 | Tutorials | Awesome-LLM-Safety | link |
| 24.01 | Tutorials | Awesome-LM-SSP | link |
👉Latest&Comprehensive Security Paper
| Date | Institute | Publication | Paper |
|---|---|---|---|
| 19.12 | Microsoft | CCS2020 | Analyzing Information Leakage of Updates to Natural Language Models |
| 21.07 | Google Research | ACL2022 | Deduplicating Training Data Makes Language Models Better |
| 21.10 | Stanford | ICLR2022 | Large language models can be strong differentially private learners |
| 22.02 | Google Research | ICLR2023 | Quantifying Memorization Across Neural Language Models |
| 22.02 | UNC Chapel Hill | ICML2022 | Deduplicating Training Data Mitigates Privacy Risks in Language Models |
| Date | Type | Title | URL |
|---|---|---|---|
| 23.10 | Tutorials | Awesome-LLM-Safety | link |
| 24.01 | Tutorials | Awesome-LM-SSP | link |
👉Latest&Comprehensive Privacy Paper
| Date | Institute | Publication | Paper |
|---|---|---|---|
| 21.09 | University of Oxford | ACL2022 | TruthfulQA: Measuring How Models Mimic Human Falsehoods |
| 23.11 | Harbin Institute of Technology | arxiv | A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions |
| 23.11 | Arizona State University | arxiv | Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey |
| Date | Type | Title | URL |
|---|---|---|---|
| 23.07 |
Selected from shared topics, language and repository description—not editorial ratings.
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