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Found 3 papers, 3 papers with code
Learning to Check: Unleashing Potentials for Self-Correction in Large Language Models
After integrating the original CoT data and checking-correction data for training, we observe that models could improve their self-checking capabilities, thereby enhancing their self-correction capacity and eliminating the need for external feedback or ground truth labels to ascertain the endpoint of correction.
DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models
Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters.
Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text Classification
The study of human values is essential in both practical and theoretical domains.
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