Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models
arXiv:2606.24841v1 Announce Type: new Abstract: Prompt-based learning has emerged as a dominant paradigm in natural language processing. This study explores the impact of diverse pre-training objectives on the performance of encoder-decoder pre-trained language models across generation and question answering tasks, with a focus on commonsense knowledge retrieval and completion. We highlight the benefits of incorporating multiple objectives during both pre-training and fine-tuning stages. We introduce the Match Task to Objective (MTO) framework and methods for determining the appropriate object
The rapid advancement of large language models necessitates optimizing their performance for specific applications, making fine-tuning and prompt-tuning strategies critical for current and future AI development.
This research provides a framework for more efficient and effective utilization of pre-trained language models, directly impacting the capabilities and deployment of AI systems across various industries.
The understanding and application of pre-training objectives will evolve to be more task-specific, leading to more performant and specialized AI models.
- · AI developers
- · NLP researchers
- · Cloud providers with advanced AI platforms
- · SaaS companies leveraging LLMs
- · Organizations relying on generic, unoptimized LLM deployments
- · AI development lagging in advanced fine-tuning techniques
Improved performance and accuracy of specific natural language processing and generation tasks.
Accelerated development of domain-specific AI applications and agents due to more effective model customization.
Enhanced competition among language model providers based on their ability to offer advanced customization and optimization tools.
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Read at arXiv cs.AI