On Representation Redundancy in Large-Scale Instruction Tuning Data Selection
arXiv:2602.13773v1 Announce Type: new Abstract: Data quality is a crucial factor in large language models training. While prior work has shown that models trained on smaller, high-quality datasets can outperform those trained on much larger but noisy or low-quality corpora, systematic methods for industrial-scale data selection in instruction tuning remain underexplored. In this work, we study instruction-tuning data selection through the lens of semantic representation similarity and identify a key limitation of state-of-the-art LLM encoders: they produce highly redundant semantic embeddings. To mitigate this redundancy, we propose Compressed Representation Data Selection (CRDS), a novel framework with two variants. CRDS-R applies Rademacher random projection followed by concatenation of transformer hidden-layer representations, while CRDS-W employs whitening-based dimensionality reduction to improve representational quality. Experimental results demonstrate that both variants substa
arXiv:2602.13773v1 Announce Type: new Abstract: Data quality is a crucial factor in large language models training. While prior work has shown that models trained on smaller, high-quality datasets can outperform those trained on much larger but noisy or low-quality corpora, systematic methods for industrial-scale data selection in instruction tuning remain underexplored. In this work, we study instruction-tuning data selection through the lens of semantic representation similarity and identify a key limitation of state-of-the-art LLM encoders: they produce highly redundant semantic embeddings. To mitigate this redundancy, we propose Compressed Representation Data Selection (CRDS), a novel framework with two variants. CRDS-R applies Rademacher random projection followed by concatenation of transformer hidden-layer representations, while CRDS-W employs whitening-based dimensionality reduction to improve representational quality. Experimental results demonstrate that both variants substantially enhance data quality and consistently outperform state-of-the-art representation-based selection methods. Notably, CRDS-W achieves strong performance using only 3.5% of the data, surpassing the full-data baseline by an average of 0.71% across four datasets. Our code is available at https://github.com/tdano1/CRDS.
Executive Summary
The article 'On Representation Redundancy in Large-Scale Instruction Tuning Data Selection' addresses the critical issue of data quality in training large language models (LLMs). It highlights the redundancy in semantic embeddings produced by state-of-the-art LLM encoders and introduces the Compressed Representation Data Selection (CRDS) framework to mitigate this issue. The study presents two variants of CRDS—CRDS-R and CRDS-W—both of which significantly enhance data quality and outperform existing representation-based selection methods. Notably, CRDS-W achieves superior performance using only a fraction of the data, underscoring the potential for efficient and effective data selection in LLM training.
Key Points
- ▸ Data quality is paramount in LLM training, with smaller high-quality datasets often outperforming larger, noisier ones.
- ▸ State-of-the-art LLM encoders produce highly redundant semantic embeddings, limiting data selection efficiency.
- ▸ The CRDS framework introduces novel methods (CRDS-R and CRDS-W) to reduce redundancy and improve data quality.
- ▸ CRDS-W achieves strong performance with only 3.5% of the data, surpassing full-data baselines.
- ▸ The study provides empirical evidence supporting the effectiveness of CRDS over existing methods.
Merits
Innovative Framework
The CRDS framework is a novel approach to data selection, addressing a critical gap in the field. Its two variants offer practical solutions to the problem of representation redundancy.
Empirical Validation
The study provides robust experimental results across multiple datasets, demonstrating the effectiveness of CRDS in improving data quality and model performance.
Efficiency
CRDS-W's ability to achieve strong performance with a minimal subset of data highlights its potential for cost-effective and scalable LLM training.
Demerits
Limited Scope
The study focuses primarily on instruction-tuning data selection and may not fully address other aspects of data quality in LLM training, such as diversity and bias.
Generalizability
While the results are promising, the generalizability of CRDS to other types of LLMs or training scenarios remains to be thoroughly explored.
Implementation Complexity
The methods proposed, particularly CRDS-W, involve complex dimensionality reduction techniques that may require significant computational resources and expertise to implement effectively.
Expert Commentary
The article presents a significant advancement in the field of LLM training by addressing the critical issue of representation redundancy in data selection. The introduction of the CRDS framework, with its innovative use of dimensionality reduction techniques, offers a promising solution to enhance data quality and model performance. The empirical results are compelling, particularly the demonstration that CRDS-W can achieve superior performance with a minimal subset of data. This finding has profound implications for the efficiency and scalability of LLM training, potentially reducing the computational and financial costs associated with large-scale data processing. However, the study's focus on instruction-tuning data selection leaves room for exploration into other aspects of data quality, such as diversity and bias. Additionally, the generalizability of the CRDS framework to different types of LLMs and training scenarios warrants further investigation. Overall, the article makes a valuable contribution to the ongoing efforts to optimize data usage in AI, and its findings are likely to influence both practical applications and policy discussions in the field.
Recommendations
- ✓ Further research should explore the application of the CRDS framework to other types of LLMs and training scenarios to assess its generalizability.
- ✓ Future studies should investigate the impact of CRDS on data diversity and bias, ensuring that the benefits of efficient data selection extend to fairness and robustness in AI models.
