Can We Align LLMs to Honesty via Instruction Fine-Tuning? Addressing Hallucination in Large Language Models with Refusal-Aware Instruction Tuning

Researchers from the Hong Kong University of Science and Technology and the University of Illinois Urbana-Champaign have collaborated to address a challenge faced by large language models (LLMs) known as hallucination, where these models generate non-existent facts, by introducing a novel approach called Refusal-Aware Instruction Tuning (R-Tuning). The observation from the existing instruction tuning methods […]

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