📄 Research Papers on Prompt Engineering
Paper Title
Authors
Year
Highlights
Language Models are Few-Shot Learners
Brown et al. (OpenAI)
2020
Introduced in-context learning with GPT-3
Chain-of-Thought Prompting
Wei et al. (Google)
2022
Enables step-by-step reasoning for LLMs
Prompt Programming: Beyond Few-Shot
Liu et al.
2021
Explores prompt templates and tuning techniques
AutoPrompt
Shin et al.
2020
Automatically generated discrete prompts
InstructGPT (Instruction Tuning)
Ouyang et al. (OpenAI)
2022
Human-labeled instructions with SFT
Soft Prompt Tuning
Lester et al. (Google)
2021
Prompt embeddings instead of tokens
ReAct: Reasoning + Acting
Shinn et al.
2022
Tool use + chain-of-thought prompting
Self-Consistency for CoT
Wang et al. (Google)
2022
Uses multiple CoT paths for reliable output
T0 (Zero-Shot Prompt Generalization)
Sanh et al. (Hugging Face)
2021
Pretrained on hundreds of prompt tasks
Prompting vs Fine-Tuning
Min et al.
2022
Compute trade-offs across methods