Generative AI Weekly Research Highlights | July 31 -Aug 6 2023

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OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models - A replica of DeepMind's models, OpenFlamingo offers accessible, high-performance vision-language models. https://arxiv.org/pdf/2308.01390.pdf

Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text? - An exploration into ChatGPT’s ability to differentiate between AI-generated and human-written content, offering unique insights for AI detection. https://arxiv.org/pdf/2308.01284.pdf

XSTEST: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models - A tool to examine extreme safety behaviors in models, analyzing the balance between safety and utility. https://arxiv.org/pdf/2308.01263.pdf

LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs - Demonstrates LLMs' ability to comprehend and explain transparent models, enhancing data science automation. https://arxiv.org/pdf/2308.01157.pdf

SELFCHECK: USING LLMS TO ZERO-SHOT CHECK THEIR OWN STEP-BY-STEP REASONING [https://arxiv.org/pdf/2308.00436.pdf]

HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution [https://arxiv.org/pdf/2307.16883.pdf]

Does fine-tuning GPT-3 with the OpenAI API leak personally-identifiable information? [https://arxiv.org/pdf/2307.16382.pdf]

Making Metadata More FAIR Using Large Language Models [https://arxiv.org/pdf/2307.13085.pdf]

#generativeai,#promptengineering,#largelanguagemodels,#openai,#chatgpt,#gpt4