Recent advancements in fine-tuning large language models (LLMs) are addressing critical challenges related to safety, efficiency, and performance. Researchers are increasingly focused on balancing safety alignment with task utility, as traditional fine-tuning methods often compromise one for the other. New approaches, such as safety-preserving fine-tuning, aim to maintain safety without sacrificing performance, effectively mitigating risks like jailbreak attacks. Concurrently, memory efficiency has emerged as a pressing concern, with techniques like instance-aware token ditching demonstrating significant reductions in memory usage while preserving or enhancing task performance. Additionally, the exploration of parameter-efficient fine-tuning strategies is gaining traction, particularly in optimizing layer selection to minimize costs and improve deployment efficiency. These innovations not only enhance the adaptability of LLMs for various applications but also pave the way for safer and more resource-conscious implementations in commercial settings, such as customer service automation and content generation.
Fine-tuning large language models for vertical domains remains a labor-intensive and expensive process, requiring domain experts to curate data, configure training, and iteratively diagnose model beha...
Although large language models have transformed natural language processing, their computational costs create accessibility barriers for low-resource languages such as Brazilian Portuguese. This work ...
Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs. To address this challenge and democratize ...
Systematic reviews traditionally have taken considerable amounts of human time and energy to complete, in part due to the extensive number of titles and abstracts that must be reviewed for potential i...
Fine-tuning large language models (LLMs) with backpropagation achieves high performance but incurs substantial memory overhead, limiting scalability on resource-constrained hardware. Zeroth-order (ZO)...
Fine tuning has been regarded as a de facto approach for adapting large language models (LLMs) to downstream tasks, but the high training memory consumption inherited from LLMs makes this process inef...
As large language models (LLMs) continue to grow, the cost of full-parameter fine-tuning has made parameter-efficient fine-tuning (PEFT) the default strategy for downstream adaptation. Constraints fro...
Fine-tuning is an essential and pervasive functionality for applying large language models (LLMs) to downstream tasks. However, it has the potential to substantially degrade safety alignment, e.g., by...