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Internal Coherence Maximization (ICM): A Label-Free, Unsupervised Training Framework for LLMs
Post-training methods for pre-trained language models (LMs) depend on human supervision through demonstrations or preference feedback to specify desired behaviors. However, this approach faces critical limitations as tasks and model behaviors become very complex. Human supervision is unreliable in these scenarios as LMs learn to mimic mistakes in demonstrations...