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G-Loss: Graph-Guided Fine-Tuning of Language Models | AI Research

Key Takeaways

  • G-Loss: Graph-Guided Fine-Tuning of Language Models Fine-tuning pre-trained language models like BERT typically involves using loss functions that focus on i...
  • 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 learn more discriminative and robust embeddings.
  • In the majority of experimental setups, G-Loss converges faster and produces semantically coherent embedding spaces, resulting in higher classification accuracy than models fine-tuned with traditional loss functions.
  • G-Loss: Graph-Guided Fine-Tuning of Language Models Fine-tuning pre-trained language models like BERT typically involves using loss functions that focus on individual data points, such as cross-entropy.
Paper AbstractExpand

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 learn more discriminative and robust embeddings. We evaluate G-Loss on five benchmark datasets covering key downstream classification tasks: MR (sentiment analysis), R8 and R52 (topic categorization), Ohsumed (medical document classification), and 20NG (news categorization). In the majority of experimental setups, G-Loss converges faster and produces semantically coherent embedding spaces, resulting in higher classification accuracy than models fine-tuned with traditional loss functions.

G-Loss: Graph-Guided Fine-Tuning of Language Models
Fine-tuning pre-trained language models like BERT typically involves using loss functions that focus on individual data points, such as cross-entropy. While effective, these methods often overlook the broader semantic relationships between documents, treating each sample in isolation. This paper introduces G-Loss, a framework that incorporates global structural information into the fine-tuning process. By building a document-similarity graph that evolves alongside the model's embeddings, G-Loss guides the language model to create more robust and discriminative representations, leading to improved accuracy in downstream classification tasks.

Bridging Local and Global Structure

Traditional fine-tuning methods rely on local optimization, which can struggle to generalize because they do not explicitly account for how different samples relate to one another across the entire dataset. G-Loss addresses this by modeling semantic relationships through a graph. In this framework, documents are represented as nodes, and the edges between them reflect their semantic similarity. By integrating this graph structure directly into the training process, the model learns to enforce consistency not just for individual predictions, but for the overall semantic alignment of the data.

How G-Loss Works

The core of the G-Loss approach is a dynamic, self-reinforcing process. As the language model processes a minibatch of text, it generates embeddings that are used to construct a similarity graph. The framework then applies a semi-supervised Label Propagation Algorithm (LPA) to this graph. By masking a portion of the labels and asking the model to infer them based on the graph's structure, G-Loss forces the model to learn representations that respect the underlying manifold of the data.
Crucially, this process is dynamic: as the model’s embeddings improve during training, the graph structure is updated to reflect these changes. This co-evolution allows the model to continuously refine its understanding of the global semantic space, creating a feedback loop that enhances the quality of the final embeddings.

Performance and Efficiency

The researchers evaluated G-Loss across five benchmark datasets, including sentiment analysis, topic categorization, and medical document classification. The results demonstrate that G-Loss generally achieves higher classification accuracy and faster convergence compared to traditional loss functions like cross-entropy, triplet, and supervised contrastive losses. By offering two versions—G-Loss-O (which optimizes hyperparameters) and G-Loss-SQRT (which uses an analytical estimation to avoid tuning overhead)—the framework provides flexibility for different computational needs.

Key Considerations

While G-Loss offers a more comprehensive way to fine-tune language models, it is important to note that its performance is tied to the quality of the initial embeddings generated by the encoder. The framework is designed to be compatible with any transformer-based encoder, such as BERT, RoBERTa, or DistilBERT. Because the graph is constructed dynamically within each minibatch, the approach remains scalable, avoiding the high memory and computational costs associated with static, full-dataset graph methods used in previous research.

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