HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning (2412.04661v1)

Abstract: Retrieval-Augmented Generation (RAG) enhances 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 LLMs 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.

• The paper introduces HEAL, a method that aligns document embeddings hierarchically to enhance retrieval performance in RAG systems.
• It uses a novel multi-level contrastive loss with HNMFk-driven hierarchical clustering, achieving near-perfect classification metrics.
• HEAL effectively mitigates hallucinations by reducing false positives across diverse domains like healthcare and cybersecurity.

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

The paper presented introduces "Hierarchical Embedding Alignment Loss" (HEAL), a novel framework designed to enhance the retrieval capabilities and representation learning within the scope of Retrieval-Augmented Generation (RAG) systems. Through leveraging hierarchical structures in document embeddings, HEAL seeks to mitigate hallucinations commonly encountered in LLMs.

Methodology Overview

HEAL enhances embedding alignment using a structured, multi-faceted approach:

1. Hierarchical Document Clustering: Utilizing Hierarchical Non-negative Matrix Factorization (HNMF) with automatic latent feature estimation (HNMFk), the paper delineates a procedure to uncover and leverage hierarchical structures in document corpora. This method categorizes documents into nested clusters characterized by thematic coherence, aligning embeddings to preserve both coarse-grained and fine-grained document similarities.
2. Hierarchical Multilevel Contrastive Loss: This contrastive loss function is refined to account for hierarchical label structures. The loss is calculated at multiple hierarchical levels, with specific penalties employed to emphasize distinctions across levels. This enables precise alignment of embeddings with document clusters derived from HNMF.
3. Fine-tuning for RAG: HEAL's functionalities are integrated into RAG systems to fine-tune embeddings. The framework uses a pre-existing model (SciNCL) to generate embeddings subsequently fine-tuned by minimizing HEAL loss through gradient-based optimization. This process aligns query and document embeddings with hierarchical structures, thereby improving retrieval efficiency and accuracy.

Experimental Evaluation

HEAL is validated through comprehensive experimentation across datasets from diverse domains—Healthcare, Material Science, Applied Mathematics, and Cyber-security. Hierarchical classification, retrieval accuracy, and hallucination metrics were systematically benchmarked against baseline methods. The findings demonstrated substantial performance improvements:

• Classification and Retrieval: There was a notable enhancement in hierarchical classification accuracy (e.g., F1 scores for Material Science data reached near-perfect levels at 0.99). Retrieval precision metrics, such as MRR and nDCG, showed significant improvements, indicating enhanced relevance and ranking of retrieved documents.
• Hallucination Mitigation: Reductions in hallucination rates were markedly evident across all datasets, with particular improvements in lowering false positives and severity measures. This suggests HEAL's effective role in eliminating the retrieval of irrelevant or misleading content, crucial for the factual accuracy of LLM outputs.

Implications and Future Directions

The introduction of HEAL provides compelling evidence for the utility of hierarchical embedding alignment in improving the performance of RAG systems. By structuring embeddings to align with the inherent hierarchical organization of domain-specific datasets, HEAL directly enhances retrieval relevance and accuracy, thus reducing the propensity for hallucinations in LLM-generated outputs.

The theoretical implications of this work extend to enhancing our understanding of contrastive learning's role within hierarchical classification and retrieval contexts. Practically, the HEAL framework could be further tailored and deployed across various knowledge-intensive applications that demand high precision and accuracy.

Looking forward, exploring further hybridization of HEAL with other unsupervised or semi-supervised learning paradigms could yield even more powerful models. Furthermore, extending HEAL's capabilities to align with multiple types of hierarchies or integrating it within larger multi-model systems presents an intriguing avenue for future research development. The potential to apply this methodology across broader contexts, such as multimedia retrieval or dynamic knowledge bases, exemplifies the adaptability and scope of HEAL's foundational principles in revolutionizing retrieval-enhanced insights in AI applications.