… affect the performance of chain-of-thought prompting and require large … prompt engineering
costly and motivate us to find an automatic and task-agnostic way to adapt chain-ofthought …

… affect the performance of chain-of-thought prompting and require large … prompt engineering
costly and motivate us to find an automatic and task-agnostic way to adapt chain-ofthought …

… appended to the original inputs, marked by a special token, to … Logic-driven context extension
and data augmentation for … the public GPT3 API, we select a subset of 5000 samples (the …

… Automatic prompt augmentation and selection with chain-of-thought from labeled data. In
Findings of the Association for Computational Linguistics: EMNLP 2023, pages 12113–12139. …

… to perform data augmentation tasks to generate labeled training data … Chain-of-Thought (CoT)
prompting for few-shot prompts. CoT … The selection of the appropriate prompting technique …

… introducing Chain-of-Thought prompting, we first give a definition of CoT prompts. CoT …
Models need to select informative materials and conduct reasoning on these materials. CoT …

… additional prompt-tuning techniques: Chain of Thought (CoT) … re-label the training data using
the newly created prompt (… ranking and selecting the metrics for prompt augmentation. For …

… additional prompt-tuning techniques, Chain of Thought (CoT) … re-label the training data using
the newly created prompt (… ranking and selecting the metrics for prompt augmentation. For …

… the selection process itself and the construction of the prompt … LLM after going through the
chain of thought. So we make here … that if the intermediate generated data leads to the correct …

… human-labeled data, including target domain gold data. We … chain-ofthought prompting
methods with LM augmentation for … prompt for ss that instructs a FLAN-UL2-20B model to select …