# Instruction Mining: Instruction Data Selection for Tuning Large Language Models

arXiv: https://arxiv.org/abs/2307.06290 · HTML: https://mlsanity.com/paper/2307.06290/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [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)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023)
- [Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor](https://mlsanity.com/paper/2212.09689/) (2022)
- [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://mlsanity.com/paper/2210.07197/) (2022)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)

## Built on by (most cited first, 30 of 58)

- [Efficient Large Language Models: A Survey](https://mlsanity.com/paper/2312.03863/) (2023)
- [Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning](https://mlsanity.com/paper/2402.04833/) (2024)
- [TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones](https://mlsanity.com/paper/2312.16862/) (2023)
- [LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement](https://mlsanity.com/paper/2403.15042/) (2024)
- [Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection](https://mlsanity.com/paper/2402.12501/) (2024)
- [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024)
- [Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning](https://mlsanity.com/paper/2502.01968/) (2025)
- [Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models](https://mlsanity.com/paper/2412.02980/) (2024)
- [Rethinking the Instruction Quality: LIFT is What You Need](https://mlsanity.com/paper/2312.11508/) (2023)
- [Aligning Large Language Models with Human: A Survey](https://mlsanity.com/paper/2307.12966/) (2023)
- [Improving Data Efficiency via Curating LLM-Driven Rating Systems](https://mlsanity.com/paper/2410.10877/) (2024)
- [D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning](https://mlsanity.com/paper/2503.11441/) (2025)
- [SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning](https://mlsanity.com/paper/2405.00705/) (2024)
- [FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training](https://mlsanity.com/paper/2501.09213/) (2025)
- [Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spaces](https://mlsanity.com/paper/2501.09024/) (2024)
- [Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning](https://mlsanity.com/paper/2504.06438/) (2025)
- [Efficient Data Selection at Scale via Influence Distillation](https://mlsanity.com/paper/2505.19051/) (2025)
- [R.I.P.: Better Models by Survival of the Fittest Prompts](https://mlsanity.com/paper/2501.18578/) (2025)
- [Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data](https://mlsanity.com/paper/2502.04380/) (2025)
- [T-SHIRT: Token-Selective Hierarchical Data Selection for Instruction Tuning](https://mlsanity.com/paper/2506.01317/) (2025)
- [OPTune: Efficient Online Preference Tuning](https://mlsanity.com/paper/2406.07657/) (2024)
- [AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning](https://mlsanity.com/paper/2503.07557/) (2025)
- [Octo-planner: On-device Language Model for Planner-Action Agents](https://mlsanity.com/paper/2406.18082/) (2024)
- [HyperINF: Unleashing the HyperPower of the Schulz's Method for Data Influence Estimation](https://mlsanity.com/paper/2410.05090/) (2024)
- [Merge to Mix: Mixing Datasets via Model Merging](https://mlsanity.com/paper/2505.16066/) (2025)
- [Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning](https://mlsanity.com/paper/2505.17266/) (2025)
- [Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning](https://mlsanity.com/paper/2508.21589/) (2025)
- [SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training](https://mlsanity.com/paper/2601.23155/) (2026)
- [Baichuan Alignment Technical Report](https://mlsanity.com/paper/2410.14940/) (2024)
- [Towards Active Synthetic Data Generation for Finetuning Language Models](https://mlsanity.com/paper/2512.00884/) (2025)

## Abstract (arXiv)

Large language models (LLMs) are initially pretrained for broad capabilities and then finetuned with instruction-following datasets to improve their performance in interacting with humans. Despite advances in finetuning, a standardized guideline for selecting high-quality datasets to optimize this process remains elusive. In this paper, we first propose InstructMining, an innovative method designed for automatically selecting premium instruction-following data for finetuning LLMs. Specifically, InstructMining utilizes natural language indicators as a measure of data quality, applying them to evaluate unseen datasets. During experimentation, we discover that double descent phenomenon exists in large language model finetuning. Based on this observation, we further leverage BlendSearch to help find the best subset among the entire dataset (i.e., 2,532 out of 100,000). Experiment results show that InstructMining-7B achieves state-of-the-art performance on two of the most popular benchmarks: LLM-as-a-judge and Huggingface OpenLLM leaderboard.