# Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration

arXiv: https://arxiv.org/abs/2310.09168 · HTML: https://mlsanity.com/paper/2310.09168/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 26 of 26)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [MetaICL: Learning to Learn In Context](https://mlsanity.com/paper/2110.15943/) (2021)
- [Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision](https://mlsanity.com/paper/2305.03047/) (2023)
- [The False Promise of Imitating Proprietary LLMs](https://mlsanity.com/paper/2305.15717/) (2023)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://mlsanity.com/paper/2304.14402/) (2023)
- [Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases](https://mlsanity.com/paper/2303.14742/) (2023)
- [Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks](https://mlsanity.com/paper/2305.14201/) (2023)
- [Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation](https://mlsanity.com/paper/2305.14327/) (2023)
- [ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback](https://mlsanity.com/paper/2304.02426/) (2023)
- [Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance](https://mlsanity.com/paper/2305.13225/) (2023)
- [Phoenix: Democratizing ChatGPT across Languages](https://mlsanity.com/paper/2304.10453/) (2023)

## Built on by (most cited first, 18 of 18)

- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [FuseChat: Knowledge Fusion of Chat Models](https://mlsanity.com/paper/2408.07990/) (2024)
- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions](https://mlsanity.com/paper/2405.19744/) (2024)
- [Data Management For Training Large Language Models: A Survey](https://mlsanity.com/paper/2312.01700/) (2023)
- [SMART: Submodular Data Mixture Strategy for Instruction Tuning](https://mlsanity.com/paper/2403.08370/) (2024)
- [Instruction Tuning with Human Curriculum](https://mlsanity.com/paper/2310.09518/) (2023)
- [Safety Alignment via Constrained Knowledge Unlearning](https://mlsanity.com/paper/2505.18588/) (2025)
- [MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation](https://mlsanity.com/paper/2504.12563/) (2025)
- [ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents](https://mlsanity.com/paper/2508.20973/) (2025)
- [Say More with Less: Understanding Prompt Learning Behaviors through Gist Compression](https://mlsanity.com/paper/2402.16058/) (2024)
- [Knowledge Fusion of Chat LLMs: A Preliminary Technical Report](https://mlsanity.com/paper/2402.16107/) (2024)
- [DS2-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning](https://mlsanity.com/paper/2603.12932/) (2026)
- [CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation](https://mlsanity.com/paper/2406.07054/) (2024)
- [Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models](https://mlsanity.com/paper/2409.20135/) (2024)
- [ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs](https://mlsanity.com/paper/2506.10288/) (2025)
- [Chain-of-Specificity: An Iteratively Refining Method for Eliciting Knowledge from Large Language Models](https://mlsanity.com/paper/2402.15526/) (2024)
- [Gamayun's Path to Multilingual Mastery: Cost-Efficient Training of a 1.5B-Parameter LLM](https://mlsanity.com/paper/2512.21580/) (2025)

## Abstract (arXiv)

Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks. However, existing data employed for such tuning often exhibit an inadequate coverage of individual domains, limiting the scope for nuanced comprehension and interactions within these areas. To address this deficiency, we propose Explore-Instruct, a novel approach to enhance the data coverage to be used in domain-specific instruction-tuning through active exploration via Large Language Models (LLMs). Built upon representative domain use cases, Explore-Instruct explores a multitude of variations or possibilities by implementing a search algorithm to obtain diversified and domain-focused instruction-tuning data. Our data-centric analysis validates the effectiveness of this proposed approach in improving domain-specific instruction coverage. Moreover, our model's performance demonstrates considerable advancements over multiple baselines, including those utilizing domain-specific data enhancement. Our findings offer a promising opportunity to improve instruction coverage, especially in domain-specific contexts, thereby advancing the development of adaptable language models. Our code, model weights, and data are public at urlhttps://github.com/fanqiwan/Explore-Instruct.