Large Language Models Are Overparameterized Text Encoders (2410.14578v1)

Abstract: LLMs demonstrate strong performance as text embedding models when finetuned with supervised contrastive training. However, their large size balloons inference time and memory requirements. In this paper, we show that by pruning the last $p\%$ layers of an LLM before supervised training for only 1000 steps, we can achieve a proportional reduction in memory and inference time. We evaluate four different state-of-the-art LLMs on text embedding tasks and find that our method can prune up to 30\% of layers with negligible impact on performance and up to 80\% with only a modest drop. With only three lines of code, our method is easily implemented in any pipeline for transforming LLMs to text encoders. We also propose $\text{L}^3 \text{Prune}$, a novel layer-pruning strategy based on the model's initial loss that provides two optimal pruning configurations: a large variant with negligible performance loss and a small variant for resource-constrained settings. On average, the large variant prunes 21\% of the parameters with a $-0.3$ performance drop, and the small variant only suffers from a $-5.1$ decrease while pruning 74\% of the model. We consider these results strong evidence that LLMs are overparameterized for text embedding tasks, and can be easily pruned.

• The paper introduces a pruning method that reduces up to 30% of LLM layers with negligible performance loss for text embedding.
• It presents the L³Prune strategy that identifies optimal layer removal using initial loss, resulting in only a -0.3 performance drop.
• The approach requires minimal code changes and integrates with other methods, broadening deployment in resource-constrained settings.

Analysis of "LLMs Are Overparameterized Text Encoders"

The paper entitled "LLMs Are Overparameterized Text Encoders" addresses a prevalent issue in the field of NLP: the resource-intensive nature of LLMs. The authors propose a solution by demonstrating that these models can be significantly pruned without substantial performance degradation when applied to text embedding tasks.

Problem Statement and Contributions

The crux of the paper lies in balancing the powerful capabilities of LLMs with their high resource demands. The research questions the necessity of all layers in an LLM for the task of text embedding, suggesting that they may be overparameterized for this purpose. The key contributions of the paper are:

• Pruning Approach: The authors introduce a method to prune the last $p\%$ layers of an LLM, claiming up to 30% pruning with negligible performance loss, and up to 80% pruning with a modest decrease in performance.
• Layer-Pruning Strategy: A novel approach named L^3Prune is proposed, which uses the initial loss of the model to determine optimal pruning configurations, showing a performance drop of only $-0.3$ for a pruned model.
• Implementation Simplicity: The methodology is straightforward, requiring minimal code alterations—merely three lines—making it adaptable to existing pipelines.
• Orthogonality: Their pruning technique is compatible with other compression methods, highlighting its integration flexibility.

Experimental Verification

In their experiments, the authors tested four different LLMs, ranging from 3.8B to 8B parameters, for text embedding tasks. The results indicate:

• Resilience to Pruning: Models retained effectiveness with up to 30% layer reduction, while even significant pruning (up to 80%) did not render them ineffective, suggesting overparameterization.
• Effect of L^3Prune: The L^3Prune strategy efficiently identified two configurations (large and small) that suited different computational capacities and performance needs.

The evaluation was conducted on an extensive benchmark subset, confirming the robustness of the pruned models against a diverse range of tasks, and maintaining competitive performance metrics.

Implications and Future Directions

This research underscores a crucial insight into the overparameterization of LLMs, challenging the conventional approach of leveraging fully intact models for tasks that do not require such depth. Practically, it provides a pathway for deploying smaller, equally competent models in resource-constrained environments, thereby broadening the applicability and accessibility of LLM-based applications.

Theoretically, this could spark a re-evaluation of model architecture and build foundational understanding for more efficient LLM design. Future research could explore the underlying dynamics of layer contribution in various contexts, refine pruning strategies, and investigate the synergy between pruning and other efficiency techniques like quantization and distillation.

Conclusion

The paper offers a significant stride towards optimizing LLMs for text embedding tasks, presenting a practical and theoretically grounded method to reduce model size without compromising performance. As resource efficiency becomes increasingly critical, this work serves as a valuable reference point for ongoing and future developments in NLP and AI deployment strategies.