18 citations · 20 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 1 cited
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
Ming Li, Lichang Chen, Jiuhai Chen +4
Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation. Notably, the output control and alignment with the…
cs.AI2023★ 1 cited
How Many Demonstrations Do You Need for In-context Learning?
Jiuhai Chen, Lichang Chen, Chen Zhu +1
Large language models (LLMs) are capable to perform complex reasoning by in-context learning (ICL) when provided with a few input-output demonstrations (demos) and more powerful wh…
cs.AI2023★ 18 cited
When do you need Chain-of-Thought Prompting for ChatGPT?
Jiuhai Chen, Lichang Chen, Heng Huang +1
Chain-of-Thought (CoT) prompting can effectively elicit complex multi-step reasoning from Large Language Models~(LLMs). For example, by simply adding CoT instruction ``Let's think…