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This work systematically compares LLMaJ methods with and without decomposition on multiple NLG datasets and finds that, when human labels are available, LLMaJ without using task decomposition can perform comparably to human annotators.
NAPHA (eNtropy-Aware Post-Hoc Alignment), a simple yet effective lightweight post-hoc alignment method that matches the LLM distribution to the HJD by first assigning an instance to a discrete entropy class and then routing it to specialized, trained alignment models is proposed.
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