Unsupervised Algorithm for Language Model Fine-Tuning Introduced
By
kordlessagain
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Summary
The article introduces an unsupervised algorithm, Internal Coherence Maximization (ICM), to fine-tune pretrained language models without external supervision. It shows that this method matches or outperforms training on human supervision in various tasks, including those where language models have superhuman capabilities. Additionally, the algorithm improves the training of advanced language models and assists in tasks like Haiku generation.
Key quotes
· 3 pulledTo steer pretrained language models for downstream tasks, today's post-training paradigm relies on humans to specify desired behaviors.
Our method matches the performance of training on golden supervision and outperforms training on crowdsourced human supervision.
On tasks where LMs' capabilities are strongly superhuman, our method can elicit those capabilities significantly better than training on human labels.
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