Papers › HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning

HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning

5 Dec 2024arXiv:2412.04661archive 2025-07-28

Manish Bhattarai, Ryan Barron, Maksim Eren, Minh Vu, Vesselin Grantcharov, Ismael Boureima, Valentin Stanev, Cynthia Matuszek, Vladimir Valtchinov, Kim Rasmussen, Boian Alexandrov

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effectiveness of RAG depends on the relevance of retrieved documents, which is influenced by the semantic alignment of embeddings with the domain's specialized content. Although full fine-tuning can align language models to specific domains, it is computationally intensive and demands substantial data. This paper introduces Hierarchical Embedding Alignment Loss (HEAL), a novel method that leverages hierarchical fuzzy clustering with matrix factorization within contrastive learning to efficiently align LLM embeddings with domain-specific content. HEAL computes level/depth-wise contrastive losses and incorporates hierarchical penalties to align embeddings with the underlying relationships in label hierarchies. This approach enhances retrieval relevance and document classification, effectively reducing hallucinations in LLM outputs. In our experiments, we benchmark and evaluate HEAL across diverse domains, including Healthcare, Material Science, Cyber-security, and Applied Maths.

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Tasks

Contrastive Learning Document Classification RAG Representation Learning Retrieval Retrieval-augmented Generation

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Methods

ALIGN Adam Attention Attention Dropout BART BERT BPE Contrastive Learning Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Residual Connection Softmax Weight Decay WordPiece

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