Entropy-Based Data Selection for Language Models
arXiv:2602.17465v1 Announce Type: new Abstract: Modern language models (LMs) increasingly require two critical resources: computational resources and data resources. Data selection techniques can effectively reduce the amount of training data required for fine-tuning LMs. However, their effectiveness is closely related to computational resources, which always require a high compute budget. Owing to the resource limitations in practical fine-tuning scenario, we systematically reveal the relationship between data selection and uncertainty estimation of selected data. Although large language models (LLMs) exhibit exceptional capabilities in language understanding and generation, which provide new ways to alleviate data scarcity, evaluating data usability remains a challenging task. This makes efficient data selection indispensable. To mitigate these issues, we propose Entropy-Based Unsupervised Data Selection (EUDS) framework. Empirical experiments on sentiment analysis (SA), topic class
arXiv:2602.17465v1 Announce Type: new Abstract: Modern language models (LMs) increasingly require two critical resources: computational resources and data resources. Data selection techniques can effectively reduce the amount of training data required for fine-tuning LMs. However, their effectiveness is closely related to computational resources, which always require a high compute budget. Owing to the resource limitations in practical fine-tuning scenario, we systematically reveal the relationship between data selection and uncertainty estimation of selected data. Although large language models (LLMs) exhibit exceptional capabilities in language understanding and generation, which provide new ways to alleviate data scarcity, evaluating data usability remains a challenging task. This makes efficient data selection indispensable. To mitigate these issues, we propose Entropy-Based Unsupervised Data Selection (EUDS) framework. Empirical experiments on sentiment analysis (SA), topic classification (Topic-CLS), and question answering (Q&A) tasks validate its effectiveness. EUDS establishes a computationally efficient data-filtering mechanism. Theoretical analysis and experimental results confirm the effectiveness of our approach. EUDS significantly reduces computational costs and improves training time efficiency with less data requirement. This provides an innovative solution for the efficient fine-tuning of LMs in the compute-constrained scenarios.
Executive Summary
This article proposes Entropy-Based Unsupervised Data Selection (EUDS) framework to efficiently select data for fine-tuning large language models (LLMs) in compute-constrained scenarios. EUDS establishes a computationally efficient data-filtering mechanism, significantly reducing computational costs and improving training time efficiency with less data requirement. Empirical experiments on sentiment analysis, topic classification, and question answering tasks validate its effectiveness. The proposed framework is particularly relevant in the context of growing resource limitations and the increasing reliance on LMs for various applications. By alleviating data scarcity and computational resource constraints, EUDS provides a valuable solution for the practical fine-tuning of LMs.
Key Points
- ▸ Entropy-Based Unsupervised Data Selection (EUDS) framework is proposed to efficiently select data for fine-tuning LMs.
- ▸ EUDS establishes a computationally efficient data-filtering mechanism.
- ▸ Empirical experiments validate the effectiveness of EUDS on sentiment analysis, topic classification, and question answering tasks.
Merits
Strength in Computational Efficiency
EUDS significantly reduces computational costs and improves training time efficiency with less data requirement, making it particularly suitable for compute-constrained scenarios.
Robustness in Performance
Empirical experiments demonstrate the effectiveness of EUDS on various tasks, showcasing its robustness in performance.
Demerits
Limited Generalizability
The proposed framework's applicability to other domains or tasks beyond sentiment analysis, topic classification, and question answering remains uncertain.
Dependence on Model Complexity
EUDS' effectiveness may be heavily reliant on the complexity of the LMs being fine-tuned, which could limit its applicability to certain models or scenarios.
Expert Commentary
While the proposed Entropy-Based Unsupervised Data Selection (EUDS) framework demonstrates significant promise in addressing the challenges of data selection for LMs, its limitations and potential areas of generalizability should be carefully considered. The framework's reliance on model complexity and its applicability to other domains or tasks remain uncertain. Nevertheless, EUDS' robustness in performance and its potential to alleviate data scarcity and computational resource constraints make it a valuable contribution to the field. As the community continues to grapple with the challenges of LMs, EUDS provides a crucial step towards developing more efficient and effective solutions.
Recommendations
- ✓ Further research should focus on exploring EUDS' generalizability to other domains and tasks to fully realize its potential.
- ✓ Investigating the framework's applicability to various LMs and fine-tuning scenarios will help to better understand its robustness and limitations.
