Efficient Strategy for Improving Large Language Model (LLM) Capabilities

TL;DR

This paper presents an efficient approach to enhance large language models by combining data selection, training strategies, and architectural adjustments, aiming to improve performance in resource-limited settings.

Contribution

It introduces a systematic methodology for optimizing LLMs through data curation, training techniques, and architecture modifications, validated by comprehensive experiments.

Findings

Improved LLM performance with reduced computational resources

Enhanced response time and safety in LLM outputs

Validated strategies through comparative testing

Abstract

Large Language Models (LLMs) have become a milestone in the field of artificial intelligence and natural language processing. However, their large-scale deployment remains constrained by the need for significant computational resources. This work proposes starting from a base model to explore and combine data processing and careful data selection techniques, training strategies, and architectural adjustments to improve the efficiency of LLMs in resource-constrained environments and within a delimited knowledge base. The methodological approach included defining criteria for building reliable datasets, conducting controlled experiments with different configurations, and systematically evaluating the resulting variants in terms of capability, versatility, response time, and safety. Finally, comparative tests were conducted to measure the performance of the developed variants and to validate the effectiveness of the proposed strategies. This work is based on the master's thesis in Systems and Computer Engineering titled "Efficient Strategy for Improving the Capabilities of Large Language Models (LLMs)".