Boosting LLM Efficiency with Low-Rank Adapters and Neural Architecture Search

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Efficient Language Models: Combining Low-Rank Adapters and Neural Architecture Search
Large language models (LLMs) have revolutionized the capabilities of natural language processing. However, their impressive performance comes with a high demand for computing power and storage space, which complicates their deployment and fine-tuning for specific tasks. Research is therefore intensively looking for methods to make these models more efficient without significantly impacting their performance. A promising approach combines low-rank adapters with neural architecture search (NAS).
Low-Rank Adapters: Efficient Fine-tuning
Low-rank adapters enable so-called parameter-efficient fine-tuning (PEFT). Instead of retraining all parameters of a large language model, small, additional layers – the adapters – are integrated into the model. These adapters have a significantly smaller number of parameters and are trained specifically for the respective task, while the weights of the original model remain frozen. By reducing the trainable parameters, the computational effort for fine-tuning is considerably reduced.
Neural Architecture Search (NAS): Optimal Adaptation
Neural architecture search (NAS) automates the process of model optimization. Instead of manually testing different architectures, NAS uses algorithms to find the optimal structure of a neural network for a specific task. In the context of LLM compression, NAS can be used to determine the ideal architecture of the low-rank adapters to achieve the best balance between efficiency and performance. A particular focus is on weight-sharing super networks, which allow for efficient exploration of different adapter architectures.
Synergy Effects for Resource-Constrained Environments
The combination of low-rank adapters and NAS offers great potential for the compression and fine-tuning of LLMs. By automatically searching for optimal adapter architectures, models with reduced memory requirements and faster inference time can be created. This allows the use of LLMs even in resource-constrained environments, such as on mobile devices or embedded systems. This promotes the democratization of access to LLMs.
Research and Development
Research in this area is dynamic and promising. Current studies are investigating various NAS algorithms and their application to low-rank adapters. An important aspect is hardware-aware optimization, where the architecture search considers the specific characteristics of the target hardware to achieve the best possible performance. The development of open-source tools and libraries contributes to the dissemination and further development of these technologies.
Outlook
The combination of low-rank adapters and NAS represents an important step towards more efficient and accessible LLMs. Further research and development in this area will help to overcome the challenges in handling large language models and expand their application possibilities in various fields. From integration into mobile applications to use in complex AI systems – the future of LLMs will be significantly shaped by the efficient use of their resources.
Bibliographie: - https://huggingface.co/papers/2501.16372 - https://arxiv.org/html/2410.06479v1 - https://arxiv.org/html/2404.10934v1 - https://www.researchgate.net/publication/384769577_LLM_Compression_with_Neural_Architecture_Search - https://huggingface.co/papers - https://openreview.net/pdf?id=Ikbfl5T5Y4 - https://aclanthology.org/2024.naacl-industry.34.pdf - https://aclanthology.org/2024.lrec-main.940.pdf - https://paperswithcode.com/paper/llm-compression-with-neural-architecture - https://github.com/HuangOwen/Awesome-LLM-Compression ```