Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models

Language models are few-shot learners

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Training language models to follow instructions with human feedback

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Free dolly: Introducing the world’s first truly open instruction-tuned llm

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Mart: Improving llm safety with multi-round automatic red-teaming

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Unnatural instructions: Tuning language models with (almost) no human labor

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Llama guard: Llm-based input-output safeguard for human-ai conversations

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Beavertails: Towards improved safety alignment of llm via a human-preference dataset

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Mistral 7b

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Openassistant conversations - democratizing large language model alignment

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Efficient memory management for large language model serving with pagedattention

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Language models are homer simpson! safety re-alignment of fine-tuned language models through task arithmetic

Original

Rishabh Bhardwaj, Do Duc Anh, and Soujanya Poria. 2024 · 2024

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Under the surface: Tracking the artifactuality of llm-generated data

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Gpt is not an annotator: The necessity of human annotation in fairness benchmark construction

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Virginia K Felkner, Jennifer A Thompson, and Jonathan May. 2024 · 2024

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RLAIF: Scaling reinforcement learning from human feedback with AI feedback

Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Ren Lu, Thomas Mesnard, Johan Ferret, Colton Bishop, Ethan Hall, Victor Carbune, and Abhinav Rastogi. 2024 · 2024

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