June 10, 2024, 4:42 a.m. | Yikun Wang, Rui Zheng, Liang Ding, Qi Zhang, Dahua Lin, Dacheng Tao

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.04854v1 Announce Type: new
Abstract: As instruction-tuned large language models (LLMs) evolve, aligning pretrained foundation models presents increasing challenges. Existing alignment strategies, which typically leverage diverse and high-quality data sources, often overlook the intrinsic uncertainty of tasks, learning all data samples equally. This may lead to suboptimal data efficiency and model performance. In response, we propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios, by introducing the sample uncertainty (elicited from more capable LLMs). We implement …

abstract alignment arxiv challenges cs.cl data data sources diverse efficiency foundation instruction-tuned intrinsic language language model language models large language large language models llms performance quality quality data samples strategies tasks type uncertainty

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