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Although Large Language Models (LLMs) have demonstrated their strong capabilities in various tasks, recent work has revealed LLMs also exhibit undesirable behaviors, such as hallucination and toxicity, limiting their reliability and broader adoption.
A dictionary of modern English usage
Henry Watson Fowler · 1927
Earlier work this paper cites.
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Introduction to neurogenic communication disorders
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Generating with confidence: Uncertainty quantification for black-box large language models
Zhen Lin, Shubhendu Trivedi, and Jimeng Sun · 2023
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Automix: Automatically mixing language models
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Gemini: a family of highly capable multimodal models
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Natural plan: Benchmarking llms on natural language planning
Huaixiu Steven Zheng, Swaroop Mishra, Hugh Zhang, Xinyun Chen, Minmin Chen, Azade Nova, Le Hou, Heng-Tze Cheng, Quoc V Le, Ed H Chi, et al · 2024
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