✳flâneur — a map of the web's best reading
Strategic Dishonesty Can Undermine AI Safety Evaluations of Frontier LLMs
arxiv.org · saved by 1 readers
N/A
Explore this link on the map →related reading
- LLM Visualizationbbycroft.net
- AI Model & API Providers Analysis | Artificial Analysisartificialanalysis.ai
- GitHub - salesforce/AuditNLG: AuditNLG: Auditing Generative AI Language Modeling for Trustworthiness · GitHubgithub.com
- llm-security/README.md at main · greshake/llm-security · GitHubgithub.com
- GitHub - brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4. · GitHubgithub.com
- Parsed | Custom, interpretable AI systems that continuously learnparsed.com
- There's An AI For That® — The front page of AItheresanaiforthat.com
- Google Geminigemini.google.com
- GitHub - x1xhlol/system-prompts-and-models-of-ai-tools: FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Tragithub.com
- GitHub - PaulPauls/llama3_interpretability_sae: A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible. · GitHubgithub.com
- GitHub - inverse-scaling/prize: A prize for finding tasks that cause large language models to show inverse scaling · GitHubgithub.com
- EconEvals: Benchmarks and Litmus Tests for LLM Agents in Unknown Environmentsarxiv.org