mlsanityData to 14 Oct 2026

TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection

arXiv 2507.03673 ↗First posted 4 Jul 2025cs.CL cs.AIcited by 0

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Instruction Fine-Tuning (IFT) is crucial for aligning large language models (LLMs) with human preferences, and selecting a small yet representative subset from massive data significantly facilitates IFT in terms of both efficiency and effectiveness. Nevertheless, existing approaches suffer from two limitations: the use of simple heuristics restricts data diversity, while the singleton data quality evaluation accounts for inconsistent criteria between independent samples. To address the issues, we present TACOS, an innovative method that integrates Open Tagging and Comparative Scoring for IFT data selection. To capture data diversity, we leverage LLMs to assign open-domain tags to human queries, followed by a normalization stage to denoise the open tags and enable efficient clustering. Additionally, we suggest a comparative scoring method that allows the relative quality evaluation of samples within a cluster, avoiding inconsistent criteria seen in singleton-based evaluations. Extensive experiments across diverse datasets and LLM architectures demonstrate that TACOS outperforms existing approaches by a large margin. Notably, it achieves superior instruction-following performance on MT-Bench and ranks 1st among LLaMA2-7B-Based models on AlpacaEval 2.0, illustrating its efficacy for IFT data selection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 31 of its 44 references that are in the map. First posted on arXiv 4 Jul 2025.

Builds on

31 of 44 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning7 Feb
  2. 2023What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning25 Dec
  3. 2023Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 217 Nov
  4. 2023A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment10 Aug
  5. 2023LIMA: Less Is More for Alignment18 May
  6. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructions20 Dec
  7. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 24

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  5. 2023Mistral 7Bcited by 3,164
  6. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  7. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
  8. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 868
  9. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 739
  10. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 396
  11. 2023Orca: Progressive Learning from Complex Explanation Traces of GPT-4cited by 379
  12. 2023From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuningcited by 307
12 more
  1. 2022Demystifying Prompts in Language Models via Perplexity Estimationcited by 216
  2. 2023#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Modelscited by 109
  3. 2023MoDS: Model-oriented Data Selection for Instruction Tuningcited by 107
  4. 2023NEFTune: Noisy Embeddings Improve Instruction Finetuningcited by 100
  5. 2023Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuningcited by 58
  6. 2023Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Taskscited by 55
  7. 2023Self-Evolved Diverse Data Sampling for Efficient Instruction Tuningcited by 52
  8. 2021Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Explorationcited by 44
  9. 2023InstructCoder: Instruction Tuning Large Language Models for Code Editingcited by 29
  10. 2023Improving Translation Faithfulness of Large Language Models via Augmenting Instructionscited by 28
  11. 2024InstructPatentGPT: Training patent language models to follow instructions with human feedbackcited by 19
  12. 2022ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translationcited by 4

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  1. 2024Rethinking Data Selection at Scale: Random Selection is Almost All You Need
  2. 2025Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models
  3. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
  4. 2025LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning
  5. 2024Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models
  6. 2025Refine-n-Judge: Curating High-Quality Preference Chains for LLM-Fine-Tuning
  7. 2024SelectLLM: Can LLMs Select Important Instructions to Annotate?
  8. 2025Cream of the Crop: Harvesting Rich, Scalable and Transferable Multi-Modal Data for Instruction Fine-Tuning