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While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination.
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Training language models to follow instructions with human feedback
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Teaching language models to hallucinate less with synthetic tasks
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Chain-of-verification reduces hallucination in large language models
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Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Aligning large multimodal models with factually augmented rlhf
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Principle-driven self-alignment of language models from scratch with minimal human supervision
Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, and Chuang Gan. 2023b
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Lima: Less is more for alignment
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