June 6, 2024, 4:44 a.m. | Ziyun Cui, Ziyang Zhang, Wen Wu, Guangzhi Sun, Chao Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03199v1 Announce Type: cross
Abstract: Advances in large language models raise the question of how alignment techniques will adapt as models become increasingly complex and humans will only be able to supervise them weakly. Weak-to-Strong mimics such a scenario where weak model supervision attempts to harness the full capabilities of a much stronger model. This work extends Weak-to-Strong to WeakS-to-Strong by exploring an ensemble of weak models which simulate the variability in human opinions. Confidence scores are estimated using a …

abstract adapt advances alignment arxiv bayesian become capabilities classification cs.ai cs.cl cs.lg harness humans language language models large language large language models question raise supervision text text classification them type will

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