arXiv:2604.25853v3 Announce Type: replace Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure. We present G-Loss, a graph-guided loss function that incorporates semi-supervised label propagation to use structural relationships within the embedding manifold. G-Loss builds a document-similarity graph that captures global semantic relationships, thereby guiding the model to
The continuous drive to improve large language model performance and efficiency, coupled with advancements in graph-based methods, makes this a timely development.
This G-Loss function presents a significant methodological improvement for fine-tuning language models, potentially leading to more accurate and contextually aware AI, enhancing their utility across various applications.
The method of fine-tuning pre-trained language models now incorporates global semantic structure via graph-guided learning, moving beyond local neighborhood optimizations.
- · AI developers
- · NLP researchers
- · Companies using LLMs for complex tasks
- · Researchers reliant on traditional fine-tuning methods
- · Systems with limited computational resources
Improved performance and decreased training time for certain language model applications in complex domains.
This could accelerate the development of more sophisticated AI agents capable of understanding broader contexts.
The enhanced AI capabilities might reduce the need for highly specialized human expertise in specific white-collar sectors.
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Read at arXiv cs.CL