# Diversity Measurement and Subset Selection for Instruction Tuning Datasets

arXiv: https://arxiv.org/abs/2402.02318 · HTML: https://mlsanity.com/paper/2402.02318/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 73 references that are in the map.
- First posted on arXiv 4 Feb 2024.

## Builds on (most cited first, 30 of 45)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [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)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (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)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Deep Learning on a Data Diet: Finding Important Examples Early in Training](https://mlsanity.com/paper/2107.07075/) (2021)
- [Beyond neural scaling laws: beating power law scaling via data pruning](https://mlsanity.com/paper/2206.14486/) (2022)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)

## Built on by (most cited first, 29 of 29)

- [The Best Instruction-Tuning Data are Those That Fit](https://mlsanity.com/paper/2502.04194/) (2025)
- [Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric](https://mlsanity.com/paper/2502.17184/) (2025)
- [VoiceAgentBench: Are Voice Assistants ready for agentic tasks?](https://mlsanity.com/paper/2510.07978/) (2025)
- [Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges](https://mlsanity.com/paper/2507.19672/) (2025)
- [Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement](https://mlsanity.com/paper/2409.11378/) (2024)
- [MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space](https://mlsanity.com/paper/2504.13835/) (2025)
- [API-guided Dataset Synthesis to Finetune Large Code Models](https://mlsanity.com/paper/2408.08343/) (2024)
- [DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks](https://mlsanity.com/paper/2502.00270/) (2025)
- [CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM Wisdom](https://mlsanity.com/paper/2503.01836/) (2025)
- [The Role of Diversity in In-Context Learning for Large Language Models](https://mlsanity.com/paper/2505.19426/) (2025)
- [Skill-Targeted Adaptive Training](https://mlsanity.com/paper/2510.10023/) (2025)
- [Detecting and Suppressing Reward Hacking with Gradient Fingerprints](https://mlsanity.com/paper/2604.16242/) (2026)
- [From Macro to Micro: Probing Dataset Diversity in Language Model Fine-Tuning](https://mlsanity.com/paper/2505.24768/) (2025)
- [Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts](https://mlsanity.com/paper/2604.08519/) (2026)
- [COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation](https://mlsanity.com/paper/2412.17684/) (2024)
- [Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs](https://mlsanity.com/paper/2507.21482/) (2025)
- [Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning](https://mlsanity.com/paper/2509.07768/) (2025)
- [UpSkill: Mutual Information Skill Learning for Structured Response Diversity in LLMs](https://mlsanity.com/paper/2602.22296/) (2026)
- [Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT](https://mlsanity.com/paper/2603.09715/) (2026)
- [DataProphet: Demystifying Supervision Data Generalization in Multimodal LLMs](https://mlsanity.com/paper/2603.19688/) (2026)
- [KrishokChat: A Provenance-Traceable Multi-Task Bengali Agricultural Benchmark with Safety-Critical Chemical Advisory](https://mlsanity.com/paper/2606.29243/) (2026)
- [emb-diversity: A Tool for Embedding-Based Measurement of Data Diversity](https://mlsanity.com/paper/2607.19848/) (2026)
- [VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs](https://mlsanity.com/paper/2512.12072/) (2025)
- [The Chicken and Egg Dilemma: Co-optimizing Data and Model Configurations for LLMs](https://mlsanity.com/paper/2602.08351/) (2026)
- [Optimizing What We Trust: Reliability-Guided QUBO Selection of Multi-Agent Weak Framing Signals for Arabic Sentiment Prediction](https://mlsanity.com/paper/2603.04416/) (2026)
- [What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective](https://mlsanity.com/paper/2604.25132/) (2026)
- [EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation](https://mlsanity.com/paper/2604.26170/) (2026)
- [InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories](https://mlsanity.com/paper/2607.02010/) (2026)
- [Towards Scalable Data Diversification for Language Model Pretraining via Leverage Score Sampling](https://mlsanity.com/paper/2609.32484/) (2026)

## Abstract (arXiv)

We aim to select data subsets for the fine-tuning of large language models to more effectively follow instructions. Prior work has emphasized the importance of diversity in dataset curation but relied on heuristics such as the number of tasks. In this paper, we use determinantal point processes to capture the diversity and quality of instruction tuning datasets for subset selection. We propose to measure dataset diversity with log determinant distance that is the distance between the dataset of interest and a maximally diverse reference dataset. Our experiments demonstrate that the proposed diversity measure in the normalized weight gradient space is correlated with downstream instruction-following performance. Consequently, it can be used to inform when data selection is the most helpful and to analyze dataset curation strategies. We demonstrate the utility of our approach on various instruction tuning datasets.