A two-stage synthetic data generation method creates the CommonSyn dataset, allowing LLMs fine-tuned on it to produce more diverse and higher-quality commonsense responses than vanilla or human-data-trained models.
Brando Miranda, Alycia Lee, Sudharsan Sundar, Allison Casasola, and Sanmi Koyejo
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Reasoning in language models should be measured by the faithfulness and validity of their multi-step search processes and intermediate traces, not final-answer accuracy.
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Synthetic Data Generation for Training Diversified Commonsense Reasoning Models
A two-stage synthetic data generation method creates the CommonSyn dataset, allowing LLMs fine-tuned on it to produce more diverse and higher-quality commonsense responses than vanilla or human-data-trained models.
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Measuring AI Reasoning: A Guide for Researchers
Reasoning in language models should be measured by the faithfulness and validity of their multi-step search processes and intermediate traces, not final-answer accuracy.