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.
Contrastive Learning, Hierarchical Labels, Retrieval-Augmented Generation, Embedding Models, Document Clustering
Bhattarai, M., Barron, R., Eren, M.E., Vu, M., Grantcharov, V., Boureima, I., Stanev, V., Matuszek, C., Valtchinov, V., Rasmussen, K. and Alexandrov, B.. HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning. Under review at the In ICLR ’25 SSI-FM Workshop: 13th International Conference on Learning Representations, Workshop on Scaling Self-Improving Foundation Models without Human Supervision, Apr. 21, 2025, Singapore. 10 pages.
@inproceedings{bhattarai-etal-2025-heal,
title = "{HEAL}: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning",
author = "Bhattarai, Manish and
Barron, Ryan and
Eren, Maksim E. and
Vu, Minh N. and
Grantcharov, Vesselin and
Ismael, Ismael and
Stanev, Valentin and
Matuszek, Cynthia and
Valtchinov, Vladimir I and
Rasmussen, Kim and
Alexandrov, Boian S.",
editor = "Shi, Weijia and
Yu, Wenhao and
Asai, Akari and
Jiang, Meng and
Durrett, Greg and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke",
booktitle = "Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing",
month = may,
year = "2025",
address = "Albuquerque, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.knowledgenlp-1.19/",
doi = "10.18653/v1/2025.knowledgenlp-1.19",
pages = "205--214",
ISBN = "979-8-89176-229-9",
abstract = "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."
}