# A Practical Survey on Zero-shot Prompt Design for In-context Learning

arXiv: https://arxiv.org/abs/2309.13205 · HTML: https://mlsanity.com/paper/2309.13205/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Learning To Retrieve Prompts for In-Context Learning](https://mlsanity.com/paper/2112.08633/) (2021)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://mlsanity.com/paper/2010.15980/) (2020)
- [Reframing Instructional Prompts to GPTk's Language](https://mlsanity.com/paper/2109.07830/) (2021)
- [GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models](https://mlsanity.com/paper/2203.07281/) (2022)
- [Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://mlsanity.com/paper/2203.08410/) (2022)
- [BERTese: Learning to Speak to BERT](https://mlsanity.com/paper/2103.05327/) (2021)
- [Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?](https://mlsanity.com/paper/2212.10539/) (2022)

## Built on by (most cited first, 30 of 49)

- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [Large Language Model Agent in Financial Trading: A Survey](https://mlsanity.com/paper/2408.06361/) (2024)
- [Knowledge Graph-Guided Retrieval Augmented Generation](https://mlsanity.com/paper/2502.06864/) (2025)
- [Visual Prompting in Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2409.15310/) (2024)
- [Large Language Model Agent as a Mechanical Designer](https://mlsanity.com/paper/2404.17525/) (2024)
- [Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction](https://mlsanity.com/paper/2411.03707/) (2024)
- [RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information](https://mlsanity.com/paper/2406.11093/) (2024)
- [LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey](https://mlsanity.com/paper/2402.14558/) (2024)
- [Data Generation Using Large Language Models for Text Classification: An Empirical Case Study](https://mlsanity.com/paper/2407.12813/) (2024)
- [Which Demographics do LLMs Default to During Annotation?](https://mlsanity.com/paper/2410.08820/) (2024)
- [Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval](https://mlsanity.com/paper/2503.15548/) (2025)
- [OmniArch: Building Foundation Model For Scientific Computing](https://mlsanity.com/paper/2402.16014/) (2024)
- [AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys](https://mlsanity.com/paper/2510.26012/) (2025)
- [LLM-Net: Democratizing LLMs-as-a-Service through Blockchain-based Expert Networks](https://mlsanity.com/paper/2501.07288/) (2025)
- [CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents](https://mlsanity.com/paper/2505.13044/) (2025)
- [Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future](https://mlsanity.com/paper/2604.27924/) (2026)
- [Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding](https://mlsanity.com/paper/2506.14397/) (2025)
- [Efficient Model Selection for Time Series Forecasting via LLMs](https://mlsanity.com/paper/2504.02119/) (2025)
- [Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in Large Language Models](https://mlsanity.com/paper/2508.01554/) (2025)
- [Sophia: A Persistent Agent Framework of Artificial Life](https://mlsanity.com/paper/2512.18202/) (2025)
- [Leveraging Zero-Shot Prompting for Efficient Language Model Distillation](https://mlsanity.com/paper/2403.15886/) (2024)
- [PAFT: Prompt-Agnostic Fine-Tuning](https://mlsanity.com/paper/2502.12859/) (2025)
- [Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Reality](https://mlsanity.com/paper/2503.16457/) (2025)
- [Conversational Process Model Redesign](https://mlsanity.com/paper/2505.05453/) (2025)
- [Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity](https://mlsanity.com/paper/2507.18638/) (2025)
- [Large Language Models for Oral History Understanding with Text Classification and Sentiment Analysis](https://mlsanity.com/paper/2508.06729/) (2025)
- [Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas](https://mlsanity.com/paper/2508.11278/) (2025)
- [Relay Decoding: Concatenating Large Language Models for Machine Translation](https://mlsanity.com/paper/2405.02933/) (2024)
- [ViDAS: Vision-based Danger Assessment and Scoring](https://mlsanity.com/paper/2410.00477/) (2024)

## Abstract (arXiv)

The remarkable advancements in large language models (LLMs) have brought about significant improvements in Natural Language Processing(NLP) tasks. This paper presents a comprehensive review of in-context learning techniques, focusing on different types of prompts, including discrete, continuous, few-shot, and zero-shot, and their impact on LLM performance. We explore various approaches to prompt design, such as manual design, optimization algorithms, and evaluation methods, to optimize LLM performance across diverse tasks. Our review covers key research studies in prompt engineering, discussing their methodologies and contributions to the field. We also delve into the challenges faced in evaluating prompt performance, given the absence of a single "best" prompt and the importance of considering multiple metrics. In conclusion, the paper highlights the critical role of prompt design in harnessing the full potential of LLMs and provides insights into the combination of manual design, optimization techniques, and rigorous evaluation for more effective and efficient use of LLMs in various NLP tasks.