FineScope : Precision Pruning for Domain-Specialized Large Language Models Using SAE-Guided Self-Data Cultivation (2505.00624v1)

Abstract: Training LLMs from scratch requires significant computational resources, driving interest in developing smaller, domain-specific LLMs that maintain both efficiency and strong task performance. Medium-sized models such as LLaMA, llama} have served as starting points for domain-specific adaptation, but they often suffer from accuracy degradation when tested on specialized datasets. We introduce FineScope, a framework for deriving compact, domain-optimized LLMs from larger pretrained models. FineScope leverages the Sparse Autoencoder (SAE) framework, inspired by its ability to produce interpretable feature representations, to extract domain-specific subsets from large datasets. We apply structured pruning with domain-specific constraints, ensuring that the resulting pruned models retain essential knowledge for the target domain. To further enhance performance, these pruned models undergo self-data distillation, leveraging SAE-curated datasets to restore key domain-specific information lost during pruning. Extensive experiments and ablation studies demonstrate that FineScope achieves highly competitive performance, outperforming several large-scale state-of-the-art LLMs in domain-specific tasks. Additionally, our results show that FineScope enables pruned models to regain a substantial portion of their original performance when fine-tuned with SAE-curated datasets. Furthermore, applying these datasets to fine-tune pretrained LLMs without pruning also improves their domain-specific accuracy, highlighting the robustness of our approach. The code will be released.

Precision Pruning for Domain-Specialized LLMs

The paper "FineScope: Precision Pruning for Domain-Specialized LLMs Using SAE-Guided Self-Data Cultivation" introduces an innovative approach for developing domain-specific LLMs that are both efficient and capable of maintaining high task performance. Given the increasing computational demands associated with training large-scale models from scratch, the framework proposed by the authors addresses the need for resource-efficient adaptations suitable for specialized domains.

Approach and Methodology

FineScope stands out for its methodical approach to deriving compact, domain-optimized LLMs from larger pretrained models through a combination of Sparse Autoencoder (SAE) driven processes. Its primary components include:

1. Sparse Autoencoder-Based Data Cultivation: The paper employs Sparse Autoencoders (SAEs) to extract domain-specific subsets from extensive datasets. SAEs are known for their ability to generate compressed representations, which are harnessed in this paper to focus on the most salient features of domain data. By training SAEs on the activations from intermediate layers of a pretrained LLM, FineScope is able to partition a dataset to highlight domain-relevant information, fostering interpretability and efficient learning.
2. Structured Pruning with Domain Constraints: Once domain-specific datasets are curated, FineScope applies structured pruning, a technique that removes non-critical components of the model while preserving essential domain knowledge. This balances model efficiency and retention of key domain-specific attributes, mitigating the typical performance degradation encounters associated with extensive pruning.
3. Self-Data Distillation: To compensate for any potential information loss during pruning, the framework employs self-data distillation with SAE-curated datasets. This technique aids the pruned models in reclaiming critical domain-specific knowledge that pruning might otherwise discard. Importantly, self-data distillation is shown to enhance the performance of unpruned pretrained models as well, underscoring its versatility in improving domain accuracy.

Experimental Results

The paper provides extensive empirical evidence of FineScope’s efficacy across different domain-specific tasks. Models pruned and fine-tuned using SAE-curated datasets exhibited superior performance to several large-scale state-of-the-art LLMs. Notably, the application of structured pruning and self-data distillation allowed these models to regain a substantial portion of their performance, with improvements observed even against conventional fine-tuning methods. The gains were particularly evident in the field of mathematical reasoning tasks, where the curated datasets generated average performance improvements of 11.45 across models.

Implications and Future Directions

The findings of FineScope have both practical and theoretical implications. From a practical standpoint, this framework provides a viable path forward for developing domain-specific models without incurring exponential computational costs. Theoretically, it proposes a refined methodology for understanding how LLMs can be adapted using representational learning techniques such as SAEs and structured pruning.

Looking forward, the research paves the way for further explorations into more efficient methods of pruning and dataset curation, expanding the potential of LLMs in specialized applications. Moreover, future developments could explore automatic adaptation techniques without explicit domain sample selection, potentially allowing LLMs to autonomously evolve in response to diverse domain requirements.

In sum, FineScope represents a significant contribution to the pursuit of scalable LLM adaptations that meet the needs of specific domains, enabling robust applications where high accuracy and efficiency are paramount. The release of the FineScope code could further stimulate advancements in the sector by providing an adaptable toolset for continued research efforts.