Papers with open-ended dialogue datasets

1 papers
Comparing Bad Apples to Good Oranges Aligning Large Language Models via Joint Preference Optimization (2025.findings-acl)

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Challenge: Recent studies have shown that acquiring human preferences by comparing generations is not effective for large language models.
Approach: They propose a preference optimization objective that elicits preferences jointly over the instruction-response pairs.
Outcome: The proposed approach outperforms prior preference optimizations by 5.2% and 3.3% in summarization and open-ended dialogue datasets.

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