CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency (2025.emnlp-main)
Copied to clipboard
Zhanming Shen, Hao Chen, Yulei Tang, Shaolin Zhu, Wentao Ye, Xiaomeng Hu, Haobo Wang, Gang Chen, Junbo Zhao
| Challenge: | Existing methods for instruction tuning rely on expensive human-annotated seed data or powerful external teacher models. |
| Approach: | They propose a framework that achieves fully seed-free instruction tuning by employing a dual self-training loop where two models are bootstrapped solely from raw, unlabeled text. |
| Outcome: | The proposed framework outperforms seed-driven back-translation baselines and achieves comparable performance to strongly supervised methods. |
Similar Papers
Self-Instruct: Aligning Language Models with Self-Generated Instructions (2023.acl-long)
Copied to clipboard
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi
| Challenge: | Large “instruction-tuned” language models depend heavily on human-written instruction data . this limited quantity, diversity, and creativity hinders the generality of the tuned model . |
| Approach: | They propose a framework for improving instruction-following capabilities of pretrained language models by bootstrapping off their own generations. |
| Outcome: | The proposed framework outperforms existing public instruction datasets by 5% . it generates instructions, input, and output samples, then filters invalid or similar ones . |
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai (2024.acl-srw)
Copied to clipboard
| Challenge: | Xue et al., 2024) have demonstrated that large language models can perform at human level across multitudes of tasks and domains. |
| Approach: | They propose a seed-free framework for generating synthetic instruction-tuning data that incorporates fluency, diversity, and cultural context. |
| Outcome: | The proposed framework achieves competitive performance using only 5,000 instructions compared to state-of-the-art Thai LLMs trained on hundreds of thousands of instructions. |
Ensemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMs (2023.findings-emnlp)
Copied to clipboard
Young-Suk Lee, Md Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos, Ramón Astudillo
| Challenge: | Empirical studies with different instruction-tuned LMs show that our proposed method yields higher-quality instruction tuning data than Self-Instruct. |
| Approach: | They propose to use in-context learning techniques to train strong conversational agents . they propose to categorize and simplify ICL templates to make prompt learning easier . |
| Outcome: | Empirical results show that the proposed method yields higher-quality instruction tuning data than Self-Instruct and improves performance of both vanilla and instruction-tuned LMs. |
Incubating Text Classifiers Following User Instruction with Nothing but LLM (2024.emnlp-main)
Copied to clipboard
| Challenge: | In this paper, we aim to generate text classification data given arbitrary class definitions . Traditional supervised text classification fine-tunes models on expensive human annotation . |
| Approach: | They propose a framework that can generate text classification data given arbitrary class definitions . they use instruction-to-data mappings and in-context augmentation to refine the framework . |
| Outcome: | The proposed framework outperforms existing methods on benchmarks and training data generation by prompt engineering. |
CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)
Copied to clipboard
Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
| Challenge: | Recent work on generating diverse instructions and applying LLM to increase instruction complexity neglects downstream use cases. |
| Approach: | They propose a framework for generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. |
| Outcome: | Experiments on four open-domain instruction using the proposed framework validate the effectiveness of CodecLM over the current state-of-the-art. |
FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies explore approaches to synthesize instruction data with open-sourced LLMs but require high-quality human-crafted seed data. |
| Approach: | They propose an end-to-end framework to synthesize high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data. |
| Outcome: | The proposed framework synthesizes high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data. |
LongForm: Effective Instruction Tuning with Reverse Instructions (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Prior work on instruction tuning relies on expensive human annotation and crowd-sourced datasets with alignment issues. |
| Approach: | They propose a method to generate instructions via LLMs from human-written corpus examples using reverse instructions. |
| Outcome: | The proposed method outperforms larger language models without instruction tuning on tasks such as story/recipe generation and long-form question answering. |
Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
Copied to clipboard
| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
| Approach: | They evaluate the effectiveness of cycle training by using two models which are inverses of each other to generate text from structured data and one which generates the structured data from natural language text. |
| Outcome: | The proposed approach achieves nearly the same performance as fully supervised approaches on the WebNLG, E2E, WTQ, and WSQL datasets. |
DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods to collect high-quality instruction-response pairs suffer from unaffordable labor costs or severe hallucinations in the self-generation of LLMs. |
| Approach: | They propose a method that trains LLMs to generate instruction-response pairs based on human-written documents rather than relying solely on self-generation without context. |
| Outcome: | The proposed method outperforms existing typical methods on multiple benchmarks and shows that it is 100% scalable. |
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing large language models (LLMs) can solve graph reasoning and generation tasks with parameter updates without sacrificing performance. |
| Approach: | They propose a structured format verbalizer to unify all graph data into a universal code-like format, which can simply represent the graph without any external graph-specific encoders. |
| Outcome: | The proposed framework outperforms GPT-4 and LLaMA2 in graph reasoning and generation tasks by more than 13% and 38%, respectively. |