# Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation

arXiv: https://arxiv.org/abs/2401.06477 · HTML: https://mlsanity.com/paper/2401.06477/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 34 references that are in the map.
- First posted on arXiv 12 Jan 2024.

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

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [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)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [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)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models](https://mlsanity.com/paper/2401.01335/) (2024)
- [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://mlsanity.com/paper/2309.05653/) (2023)
- [CMMLU: Measuring massive multitask language understanding in Chinese](https://mlsanity.com/paper/2306.09212/) (2023)
- [Llemma: An Open Language Model For Mathematics](https://mlsanity.com/paper/2310.10631/) (2023)
- [HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://mlsanity.com/paper/2304.06975/) (2023)
- [Self-Alignment with Instruction Backtranslation](https://mlsanity.com/paper/2308.06259/) (2023)
- [Skywork: A More Open Bilingual Foundation Model](https://mlsanity.com/paper/2310.19341/) (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)
- [TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks](https://mlsanity.com/paper/2310.00752/) (2023)
- [SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark](https://mlsanity.com/paper/2307.15020/) (2023)
- [MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response](https://mlsanity.com/paper/2309.08730/) (2023)
- [Ada-Instruct: Adapting Instruction Generators for Complex Reasoning](https://mlsanity.com/paper/2310.04484/) (2023)
- [MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task Finetuning](https://mlsanity.com/paper/2310.09853/) (2023)

## Built on by (most cited first, 9 of 9)

- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [MAmmoTH2: Scaling Instructions from the Web](https://mlsanity.com/paper/2405.03548/) (2024)
- [I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm](https://mlsanity.com/paper/2408.08072/) (2024)
- [Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model](https://mlsanity.com/paper/2404.04167/) (2024)
- [MdEval: Massively Multilingual Code Debugging](https://mlsanity.com/paper/2411.02310/) (2024)
- [AIR: Complex Instruction Generation via Automatic Iterative Refinement](https://mlsanity.com/paper/2502.17787/) (2025)
- [Aligning Instruction Tuning with Pre-training](https://mlsanity.com/paper/2501.09368/) (2025)
- [IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2508.04632/) (2025)
- [IFEvalCode: Controlled Code Generation](https://mlsanity.com/paper/2507.22462/) (2025)

## Abstract (arXiv)

In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations. Adapting a self-training algorithm based on instruction back-translation and answer polishment, Kun leverages unlabelled data from diverse sources such as Wudao, Wanjuan, and SkyPile to generate a substantial dataset of over a million Chinese instructional data points. This approach significantly deviates from traditional methods by using a self-curation process to refine and select the most effective instruction-output pairs. Our experiments with the 6B-parameter Yi model across various benchmarks demonstrate Kun's robustness and scalability. Our method's core contributions lie in its algorithmic advancement, which enhances data retention and clarity, and its innovative data generation approach that substantially reduces the reliance on costly and time-consuming manual annotations. This methodology presents a scalable and efficient solution for improving the instruction-following capabilities of LLMs, with significant implications for their application across diverse fields. The code and dataset can be found at https://github.com/Zheng0428/COIG-Kun