# Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4

arXiv: https://arxiv.org/abs/2312.16171 · HTML: https://mlsanity.com/paper/2312.16171/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [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)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Can language models learn from explanations in context?](https://mlsanity.com/paper/2204.02329/) (2022)
- [MathPrompter: Mathematical Reasoning using Large Language Models](https://mlsanity.com/paper/2303.05398/) (2023)
- [Ask Me Anything: A simple strategy for prompting language models](https://mlsanity.com/paper/2210.02441/) (2022)
- [Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://mlsanity.com/paper/2305.06984/) (2023)
- [Guiding Large Language Models via Directional Stimulus Prompting](https://mlsanity.com/paper/2302.11520/) (2023)
- [Plum: Prompt Learning using Metaheuristic](https://mlsanity.com/paper/2311.08364/) (2023)

## Built on by (most cited first, 30 of 48)

- [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://mlsanity.com/paper/2401.03729/) (2024)
- [The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models](https://mlsanity.com/paper/2401.05618/) (2024)
- [CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences](https://mlsanity.com/paper/2403.09032/) (2024)
- [Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models](https://mlsanity.com/paper/2405.17374/) (2024)
- [Self-Reflection in LLM Agents: Effects on Problem-Solving Performance](https://mlsanity.com/paper/2405.06682/) (2024)
- [CUTE: Measuring LLMs' Understanding of Their Tokens](https://mlsanity.com/paper/2409.15452/) (2024)
- [Materials science in the era of large language models: a perspective](https://mlsanity.com/paper/2403.06949/) (2024)
- [What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use](https://mlsanity.com/paper/2409.08775/) (2024)
- [Scalable Qualitative Coding with LLMs: Chain-of-Thought Reasoning Matches Human Performance in Some Hermeneutic Tasks](https://mlsanity.com/paper/2401.15170/) (2024)
- [Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering](https://mlsanity.com/paper/2405.14383/) (2024)
- [Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability](https://mlsanity.com/paper/2406.18365/) (2024)
- [PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models](https://mlsanity.com/paper/2403.02246/) (2024)
- [Uncertainty Quantification in Large Language Models Through Convex Hull Analysis](https://mlsanity.com/paper/2406.19712/) (2024)
- [LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language](https://mlsanity.com/paper/2402.16929/) (2024)
- [GAAPO: Genetic Algorithmic Applied to Prompt Optimization](https://mlsanity.com/paper/2504.07157/) (2025)
- [Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization](https://mlsanity.com/paper/2405.20252/) (2024)
- [RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2406.12479/) (2024)
- [An Empirical Study of In-context Learning in LLMs for Machine Translation](https://mlsanity.com/paper/2401.12097/) (2024)
- [LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain](https://mlsanity.com/paper/2402.14871/) (2024)
- [Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers](https://mlsanity.com/paper/2503.01163/) (2025)
- [Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation](https://mlsanity.com/paper/2411.10129/) (2024)
- [LLM Sensitivity Evaluation Framework for Clinical Diagnosis](https://mlsanity.com/paper/2504.13475/) (2025)
- [What Makes a Good Natural Language Prompt?](https://mlsanity.com/paper/2506.06950/) (2025)
- [Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction](https://mlsanity.com/paper/2406.08183/) (2024)
- [Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questions](https://mlsanity.com/paper/2502.03804/) (2025)
- [What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models](https://mlsanity.com/paper/2403.13513/) (2024)
- [Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts](https://mlsanity.com/paper/2409.13449/) (2024)
- [An Agentic Approach to Generating XAI-Narratives](https://mlsanity.com/paper/2603.20003/) (2026)
- [Online Learning Defense against Iterative Jailbreak Attacks via Prompt Optimization](https://mlsanity.com/paper/2510.17006/) (2025)
- [Enhancing Explainability in Multimodal Large Language Models Using Ontological Context](https://mlsanity.com/paper/2409.18753/) (2024)

## Abstract (arXiv)

This paper introduces 26 guiding principles designed to streamline the process of querying and prompting large language models. Our goal is to simplify the underlying concepts of formulating questions for various scales of large language models, examining their abilities, and enhancing user comprehension on the behaviors of different scales of large language models when feeding into different prompts. Extensive experiments are conducted on LLaMA-1/2 (7B, 13B and 70B), GPT-3.5/4 to verify the effectiveness of the proposed principles on instructions and prompts design. We hope that this work can provide a better guide for researchers working on the prompting of large language models. Project page is available at https://github.com/VILA-Lab/ATLAS.