DEFT: Data-Efficient Fine-Tuning Through Multi-Dimensional Data Selection
文献类型：期刊论文
| 作者 | Dai, Shaojie1,2,3; Liu, Xin2; Yu, Yue2 |
| 刊名 | IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
 |
| 出版日期 | 2026 |
| 卷号 | 34页码:352-363 |
| 关键词 | Data models Complexity theory Training data Training Speech processing Semantics Adaptation models Tuning Measurement Large language models Data-efficient fine-tuning large language models (LLMs) instruction fine-tuning data selection |
| DOI | 10.1109/TASLPRO.2025.3642562 |
| 英文摘要 | Instruction tuning has emerged as a predominant method for adapting large language models (LLMs) to downstream tasks, with prevailing approaches predominantly relying on scaling up instruction data to enhance model performance. However, growing evidence suggests that indiscriminate data scaling may yield suboptimal results, as the absence of systematic evaluation criteria often leads to redundant or low-quality samples in instruction datasets. Consequently, in this paper, we propose DEFT, a multi-dimensional data selection framework that assesses instruction data from four perspectives: complexity, quality, knowledge and diversity. For complexity and quality, we develop Evol-Ranking to distill ranking capabilities from teacher models (e.g., gpt-3.5-turbo) to specialized student models. Furthermore, we propose refinement distillation to progressively optimize the student model. For knowledge, we define the average negative log-probability of text on a given LLM as knowledge, providing model-aware measurement. For diversity, we first obtain semantic representation of each sample, then calculate the similarity between samples. Finally, we ensemble all dimensions mentioned above through an ensemble scoring mechanism to select the data for instruction fine-tuning. Extensive experiments performed on MT-Bench and AlpacaEval demonstrate that DEFT performs better or on pair with the state-of-the-art open-source alignment models with only 6,000 SFT training samples. |
| 资助项目 | National Key Research and Development Program of China[2022ZD0115301] ; National Natural Science Foundation of China[62206140] ; Major Key Project of PCL[PCL2023A09] ; Major Key Project of PCL[PCL2025A11] ; OpenI Community |
| WOS研究方向 | Acoustics ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001655664500002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42919]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Liu, Xin; Yu, Yue |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, State Key Lab AI Safety, Beijing 100190, Peoples R China 2.Peng Cheng Lab, Shenzhen 518000, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Dai, Shaojie,Liu, Xin,Yu, Yue. DEFT: Data-Efficient Fine-Tuning Through Multi-Dimensional Data Selection[J]. IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2026,34:352-363. |
| APA | Dai, Shaojie,Liu, Xin,&Yu, Yue.(2026).DEFT: Data-Efficient Fine-Tuning Through Multi-Dimensional Data Selection.IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,34,352-363. |
| MLA | Dai, Shaojie,et al."DEFT: Data-Efficient Fine-Tuning Through Multi-Dimensional Data Selection".IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 34(2026):352-363. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。