# AutoHint: Automatic Prompt Optimization with Hint Generation

arXiv: https://arxiv.org/abs/2307.07415 · HTML: https://mlsanity.com/paper/2307.07415/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 23 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 20 of 20)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://mlsanity.com/paper/2104.06599/) (2021)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://mlsanity.com/paper/2209.14610/) (2022)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Large Language Model Guided Tree-of-Thought](https://mlsanity.com/paper/2305.08291/) (2023)
- [Optimizing Prompts for Text-to-Image Generation](https://mlsanity.com/paper/2212.09611/) (2022)
- [Instruction Induction: From Few Examples to Natural Language Task Descriptions](https://mlsanity.com/paper/2205.10782/) (2022)
- [TEMPERA: Test-Time Prompting via Reinforcement Learning](https://mlsanity.com/paper/2211.11890/) (2022)
- [Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling](https://mlsanity.com/paper/2305.09993/) (2023)

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

- [Efficient Prompting Methods for Large Language Models: A Survey](https://mlsanity.com/paper/2404.01077/) (2024)
- [Are Large Language Models Good Prompt Optimizers?](https://mlsanity.com/paper/2402.02101/) (2024)
- [Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization](https://mlsanity.com/paper/2406.15708/) (2024)
- [Instance-adaptive Zero-shot Chain-of-Thought Prompting](https://mlsanity.com/paper/2409.20441/) (2024)
- [Customizing Language Model Responses with Contrastive In-Context Learning](https://mlsanity.com/paper/2401.17390/) (2024)
- [Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absent](https://mlsanity.com/paper/2502.11267/) (2025)
- [LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language](https://mlsanity.com/paper/2402.16929/) (2024)
- [Prompt Obfuscation for Large Language Models](https://mlsanity.com/paper/2409.11026/) (2024)
- [SCULPT: Systematic Tuning of Long Prompts](https://mlsanity.com/paper/2410.20788/) (2024)
- [TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions](https://mlsanity.com/paper/2403.18426/) (2024)
- [Learning from Contrastive Prompts: Automated Optimization and Adaptation](https://mlsanity.com/paper/2409.15199/) (2024)
- [Optimizing LLM-Based Multi-Agent System with Textual Feedback: A Case Study on Software Development](https://mlsanity.com/paper/2505.16086/) (2025)
- [A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm](https://mlsanity.com/paper/2502.18746/) (2025)
- [Autonomous Prompt Engineering in Large Language Models](https://mlsanity.com/paper/2407.11000/) (2024)
- [Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts](https://mlsanity.com/paper/2409.13449/) (2024)
- [Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts](https://mlsanity.com/paper/2504.21117/) (2025)
- [PromptCOS: Towards Content-only System Prompt Copyright Auditing for LLMs](https://mlsanity.com/paper/2509.03117/) (2025)
- [Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation](https://mlsanity.com/paper/2311.09684/) (2023)
- [Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity](https://mlsanity.com/paper/2509.22054/) (2025)
- [Evolutionary Prompt Optimization Discovers Emergent Multimodal Reasoning Strategies in Vision-Language Models](https://mlsanity.com/paper/2503.23503/) (2025)
- [A Hierarchical Framework for Measuring Scientific Paper Innovation via Large Language Models](https://mlsanity.com/paper/2504.14620/) (2025)
- [Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs](https://mlsanity.com/paper/2506.06401/) (2025)
- [GreenTEA: Gradient Descent with Topic-modeling and Evolutionary Auto-prompting](https://mlsanity.com/paper/2508.16603/) (2025)
- [MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization](https://mlsanity.com/paper/2510.16635/) (2025)
- [TAHOE: Text-to-SQL with Automated Hint Optimization from Experience](https://mlsanity.com/paper/2606.12387/) (2026)
- [Reinforcement Learning-Based Prompt Template Stealing for Text-to-Image Models](https://mlsanity.com/paper/2510.00046/) (2025)
- [QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code](https://mlsanity.com/paper/2511.01183/) (2025)
- [AsynDBT: Asynchronous Distributed Bilevel Tuning for efficient In-Context Learning with Large Language Models](https://mlsanity.com/paper/2602.17694/) (2026)
- [Strategy-Induct: Task-Level Strategy Induction for Instruction Generation](https://mlsanity.com/paper/2605.20924/) (2026)
- [Towards Spec Learning: Inference-Time Alignment from Preference Pairs](https://mlsanity.com/paper/2606.24004/) (2026)

## Abstract (arXiv)

This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability in achieving high-quality annotation in various tasks, the key to applying this ability to specific tasks lies in developing high-quality prompts. Thus we propose a framework to inherit the merits of both in-context learning and zero-shot learning by incorporating enriched instructions derived from input-output demonstrations to optimize original prompt. We refer to the enrichment as the hint and propose a framework to automatically generate the hint from labeled data. More concretely, starting from an initial prompt, our method first instructs a LLM to deduce new hints for selected samples from incorrect predictions, and then summarizes from per-sample hints and adds the results back to the initial prompt to form a new, enriched instruction. The proposed method is evaluated on the BIG-Bench Instruction Induction dataset for both zero-shot and few-short prompts, where experiments demonstrate our method is able to significantly boost accuracy for multiple tasks.