E -Verify: A Paradigm Shift to Scalable Embedding-based Factuality Verification (2025)

Abstract: Large language models (LLMs) exhibit remarkable text-generation capabilities, yet struggle with factual consistency, motivating growing interest in factuality verification. Existing factuality verification methods typically follow a Decompose-Then-Verify paradigm, which improves granularity but suffers from poor scalability and efficiency. We propose a novel paradigm that shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space, effectively mitigating the scalability bottlenecks and computational inefficiencies inherent to prior approaches. While the proposed paradigm promises scalable verification, its implementation faces three practical challenges: efficient decomposition, factually faithful embedding, and accurate verification in embedding space. To address these challenges, we introduce E-Verify , a lightweight framework that resolves them through three specially designed modules, each aligned with a specific stage of the paradigm and designed to preserve scalability and efficiency. Experiments demonstrate that E-Verify significantly improves both decomposition and verification efficiency while maintaining competitive accuracy. These results confirm that the proposed paradigm enables scalable and fine-grained factuality verification with minimal performance trade-offs.

## introduction large language models (llms) have demonstrated remarkable capabilities in text generation tasks @xcite @xcite . nonetheless, llms often generate content with hallucinations, including incorrect dates, numerical errors, and fabricated relationships, which can mislead decision-making and exacerbate misinformation spread @xcite @xcite . this raises an urgent need for factuality verification systems that can evaluate the factual consistency of llm-generated content, especially in knowledgeintensive scenarios @xcite @xcite . a dominant line of research in factuality verification foot_0 adopts the decompose-then-verify paradigm, shown in figure 1 (top half), which decomposes generated text into atomic facts and verifies them against reference sources using llms or natural language inference (nli) models @xcite @xcite . while this paradigm enhances granularity and interpretability, the inherent pairwise verification-where each fact must be individually compared to all reference segments-leads to quadratic computational overhead, which quickly becomes prohibitively expensive for long generations, posing a critical obstacle to scalability. we begin with the observation that atomic facts are typically short and structurally simple, making them well-suited for semantic embedding. this insight motivates a shift in verification strategy: instead of performing pairwise reasoning at the text level, we shift verification to alignment in embedding space. to this end, we propose the decompose-embed-interact paradigm, shown in figure 1 (bottom half), which reframes factuality verification as a modular process of atomic decomposition, independent embedding, and lightweight interaction. by encoding facts into dense vectors and verifying them efficiently in embedding space, this paradigm eliminates the need for costly llm or nli-based cross-encoding, enabling scalable and fine-grained consistency assessment. while the proposed paradigm theoretically enables scalable factuality verification, its practical implementation poses several concrete challenges: how to decompose long-form text efficiently, how to preserve factual precision in embeddings, and how to conduct accurate verification in embedding space. to address these issues, we introduce e-verify-an efficient and embedding-based factuality verification framework for llms. e-verify operationalizes the proposed paradigm through three carefully designed modules: (1) a sentencelevel atomic decomposer based on a fine-tuned small language model (slm) improves decomposition efficiency for long-form text; (2) a bi-encoder embedder augmented with pooling-based multi-head attention enhances the factual fidelity of atomic fact embeddings beyond simple pooling; experimental results confirm the effectiveness of our framework, demonstrating substantial gains in decomposition and verification efficiency while maintaining competitive accuracy. importantly, our study reveals a key insight: embedding models, when paired with structured atomic decomposition and lightweight interaction modules, can deliver fine-grained factual verification performance previously thought to require deep cross-encodinghighlighting the potential of e-verify as a scalable alternative to traditional nli-based pipelines. our contributions can be summarized as: ## decompose-embed-interact paradigm we begin with a central observation: atomic facts are structurally simple and semantically compact, typically taking the form of short declarative sentences expressing a single verifiable proposition (see figure 2 , stage 1). this localized, contextindependent structure aligns well with modern sentence embedding models, which are designed to encode bounded propositions into fixed-length vectors. such simplicity allows atomic facts to be faithfully compressed into embeddings with minimal semantic loss, making factuality verification possible through lightweight embedding-level interactions. crucially, this enables scalable verification by avoiding the quadratic cost of cross-encoding each reference-fact pair. motivated by this observation, we propose the decompose-embed-interact paradigm, which reframes factuality verification as a modular, embedding-native process. given generated content g and reference material r, the process unfolds in three stages: decompose: decompose g and r into atomic fact sets, ## e-verify while the proposed paradigm provides a conceptual blueprint for efficient verification in embedding space, its practical implementation poses three key challenges: (1) achieving efficient decomposition of long-form text, (2) preserving factual fidelity during embedding, and (3) verifying factual consistency via accurate embedding-level interactions. we present e-verify, addressing these challenges through three carefully designed modules, as illustrated in figure 2 . we provide detailed descriptions of each component below, with implementation settings provided in appendix a. ## experiments to evaluate the effectiveness of the e-verify framework, we conduct experiments across four key dimensions: decomposition quality: compare various models to identify the most effective atomic fact decomposer. ## conclusion we propose e-verify, a lightweight framework that redefines factuality verification through a novel decompose-embed-interact paradigm. by decoupling decomposition, embedding, and interaction, e-verify replaces costly cross-encoding with efficient embedding-space alignment. experiments show that e-verify significantly improves computational efficiency while maintaining competitive accuracy. these results validate the paradigm's practical value and highlight the potential of embeddingnative verification as a scalable solution for realworld factuality verification tasks.