In this paper, We introduce AS-ES learning, a method to improve Chain-of-Thought (CoT) capabilities in small models by better utilizing existing CoT data, rather than merely increasing data from Large Language Models. This approach outperforms traditional training methods on tasks requiring extensive CoT, without needing extra data or model modifications, and provides insights into the mechanics of CoT learning.

In this paper, we propose a method to enhance in-silico molecule discovery in low-resource settings by using pseudo data generated by Large Language Models (LLMs), demonstrating its efficiency through improved performance, smaller model size, less data, and lower costs, with benefits increasing as more pseudo data is used.

In this report, we introduce the technical details of HuaTuo, a fine-tuned LLaMA Large Language Model, enhanced with generated QA pairs for improved performance in biomedical tasks. This approach equips HuaTuo with more accurate medical knowledge, addressing the expertise gap in standard LLMs.

In this paper, we introduce fMRI2text, a novel open-vocabulary task for converting fMRI time series to human language, and presents UniCoRN, a model that decodes text from fMRI and EEG signals better than the previous baseline, demonstrating the potential for real-world brain-computer interface applications.

In this paper, we introduce a new method to enhance prompt tuning in pre-trained models by using the Global Prompt Cell (GPC), a module that maintains prompt information throughout all encoder layers, leading to a 5.8% performance boost on SuperGLUE datasets.