Struc-EMB: The Potential of Structure-Aware Encoding in Language Embeddings

2025-10-09

AI Summary

Current LLM embedding methods process only raw text, ignoring structural information such as hyperlinks and citations, resulting in insufficient contextual awareness. To address this, we propose a structure-aware endogenous encoding paradigm that intrinsically incorporates structural relations directly into the LLM’s encoding process—bypassing post-hoc aggregation. Methodologically, we design context distillation and semantic balancing mechanisms to suppress noise and systematically compare two structural integration strategies: sequential concatenation versus parallel caching. Leveraging zero-shot learning, our approach enables end-to-end structured modeling across retrieval, clustering, classification, and recommendation tasks. Experiments demonstrate consistent and significant improvements over both plain-text baselines and diverse post-processing approaches across multiple tasks, validating the method’s effectiveness, robustness, and scalability.

Technology Category

• Machine Learning: Structured Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Text embeddings from Large Language Models (LLMs) have become foundational for numerous applications. However, these models typically operate on raw text, overlooking the rich structural information, such as hyperlinks or citations, that provides crucial context in many real-world datasets. This paper introduces and systematically evaluates a new paradigm for generating structure-aware text embeddings by integrating these structural relations directly into the LLM's internal encoding process, rather than relying on traditional post-hoc aggregation. We investigate two primary in-process methods: sequential concatenation and parallel caching. Through extensive zero-shot experiments across retrieval, clustering, classification, and recommendation tasks, we demonstrate that our structure-aware approaches consistently outperform both text-only and post-hoc baselines. Our analysis reveals critical trade-offs: sequential concatenation excels with noisy, moderate-length contexts, while parallel caching scales more effectively to long, high-signal contexts but is more susceptible to distractors. To address the challenge of noisy structural data, we also introduce and validate two effective techniques: Context Distillation and Semantic Balancing. This work provides the first comprehensive analysis of in-process structure-aware encoding, offering a blueprint for building more powerful and contextually aware embedding models.

Problem

• Integrating structural relations into LLM encoding process
• Overcoming limitations of text-only embeddings in structured datasets
• Addressing noise challenges in structure-aware embedding methods

Innovation

• Integrates structural relations into LLM internal encoding
• Uses sequential concatenation and parallel caching methods
• Introduces Context Distillation and Semantic Balancing techniques