Efficiency and Performance Optimization in Large Language Models through IB Fine-Tuning

In the rapidly evolving field of Natural Language Processing (NLP), optimizing methods for fine-tuning Large Language Models (LLMs) is increasingly critical for improving generalization and performance. Fine-tuning LLMs is challenging due to high costs, overfitting, and difficulty adapting to diverse tasks. These challenges grow as LLMs scale, making traditional fine-tuning methods inefficient and expensive. To address these issues, a novel Information Bottleneck (IB) method for fine-tuning LLMs is proposed, focusing on retaining only the most critical and relevant information in the model’s internal representations. By striking a balance between information compression and predictive relevance, the IB method aims to reduce overfitting and enhance generalization. This approach also integrates reinforcement learning and continual learning to enhance LLM performance further. The proposed framework considers two key metrics: (1) compression effectiveness, which reduces redundancy and improves generalization, and (2) predictive relevance, which ensures high task-specific performance. The proposed scheme achieves scalable fine-tuning across diverse NLP tasks using a lightweight proxy model to enhance computational efficiency. The proposed framework empirical evaluations and ablation studies show that the IB method improves accuracy while significantly reducing computational costs, enabling efficient, interpretable, and adaptable LLM optimization and increasing convergence.