When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation
The EVOREC framework combines model editing and FA-based constrained decoding for adaptive service recommendation, achieving 25.9% improvement in Recall@5.
Key Findings
Methodology
This study introduces EVOREC, an evolution-aware framework that integrates localized model editing via the ROME algorithm with FA-guided constrained decoding. The approach involves identifying knowledge points within the LLM, performing low-rank parameter updates to inject updated service facts, and encoding service interface constraints into a Trie and FA structure. During inference, the FA restricts token generation to valid sequences, reducing hallucinations and duplicates. Retrieval-augmented prompts supply relevant examples, enhancing adaptability. Extensive experiments on real-world datasets demonstrate a 25.9% relative improvement in Recall@5 over baselines, with superior performance in service evolution scenarios, outperforming fine-tuning by 22.3%.
Key Results
- On datasets from ProgrammableWeb, EVOREC achieved Recall@5 of 78.4%, surpassing traditional methods by 25.9%. FA constraints increased valid sequence generation to over 95%, significantly reducing invalid or duplicate services. Model editing enabled rapid adaptation to API changes, saving computational costs. Ablation studies confirmed that retrieval augmentation and FA constraints contributed substantially to performance gains. The framework maintained high accuracy across evolving service environments, demonstrating robustness and scalability.
Significance
This work addresses critical challenges in deploying large language models for dynamic service ecosystems. By enabling efficient, localized knowledge updates and enforcing structural constraints, EVOREC offers a practical solution for maintaining high-quality, up-to-date service recommendations without costly retraining. It bridges the gap between static pretraining and real-time service evolution, paving the way for intelligent, self-maintaining service systems. The approach enhances both the reliability and adaptability of AI-driven recommendation engines, with broad implications for industrial applications such as API management, automated service composition, and cloud service orchestration.
Technical Contribution
The paper introduces a novel combination of model editing (ROME) and FA-based constrained decoding, providing a lightweight, scalable method for knowledge updating and sequence validation. The low-rank update mechanism allows targeted injection of new service facts, while the Trie and FA structures enforce syntactic and semantic constraints during autoregressive generation. This integration ensures the generated service sequences are both valid and contextually relevant, significantly reducing hallucinations. The framework’s modular design facilitates easy extension and deployment in real-world systems, representing a substantial advancement over existing fine-tuning or rule-based approaches.
Novelty
This research is the first to unify model editing with formal structure constraints for service recommendation in evolving ecosystems. Unlike prior work focusing solely on knowledge injection or rule-based filtering, EVOREC combines localized parameter updates with automaton-guided decoding, achieving both rapid adaptation and guaranteed sequence validity. This dual strategy addresses the core issues of knowledge staleness and hallucination, setting a new standard for dynamic, reliable AI-driven service composition. Its innovative use of FA structures for real-time constraint enforcement in autoregressive models marks a significant leap forward in the field.
Limitations
- The framework relies on predefined service lexicons and interface schemas; if APIs undergo drastic changes or new services are introduced without updates, performance may decline.
- Frequent knowledge updates via model editing can accumulate errors over time, potentially affecting long-term stability.
- FA-guided decoding introduces additional computational overhead, which may impact real-time deployment in high-throughput environments.
Future Work
Future directions include developing adaptive, online knowledge base expansion mechanisms, optimizing FA structures for faster decoding, and integrating multi-modal data to enhance understanding. Exploring reinforcement learning for dynamic constraint adjustment and extending the framework to multi-turn dialogue-based service recommendation are promising avenues. Additionally, scaling the approach for large industrial deployments and ensuring robustness under diverse service ecosystems will be key to broader adoption.
AI Executive Summary
In the rapidly evolving landscape of software services, traditional recommendation systems struggle to keep pace with frequent API updates, deprecations, and new service introductions. Static models trained on historical data quickly become outdated, leading to unreliable suggestions and increased maintenance costs. Addressing this challenge, the EVOREC framework innovatively combines localized model editing with formal structural constraints to enable dynamic, accurate service recommendation.
The core of EVOREC involves two key components: first, a model editing module based on the ROME algorithm, which performs targeted, low-rank parameter updates to inject fresh service knowledge without retraining the entire model. This allows the system to adapt swiftly to API changes, ensuring the internal knowledge base remains current. Second, a FA-guided constrained decoding mechanism, which employs Trie and finite automaton structures to enforce syntactic and semantic validity during autoregressive generation. This effectively prevents hallucinations, invalid calls, and duplicate services, ensuring the output sequences are both correct and executable.
Experimental results on real-world datasets from ProgrammableWeb demonstrate the framework’s effectiveness, with Recall@5 improving by 25.9% over baseline methods. The FA constraints increased valid sequence generation to over 95%, significantly reducing errors. Moreover, EVOREC outperformed traditional fine-tuning approaches by 22.3% in service evolution scenarios, showcasing its strong adaptability.
This work offers a practical, scalable solution for maintaining high-quality service recommendations amid rapid ecosystem changes. Its innovative integration of model editing and formal constraints paves the way for intelligent, self-updating AI systems capable of supporting industrial-scale service management. Future research will focus on enhancing the system’s scalability, real-time performance, and multi-modal capabilities, further bridging the gap between AI research and industrial deployment.
Deep Dive
Abstract
The rapid evolution of software services poses substantial challenges to the design and implementation of effective recommendation systems. Traditional service recommendation approaches often rely on static representations and historical usage data, which are insufficient for adapting to the dynamic and evolving nature of service ecosystems. Recently, large language models (LLMs) have shown strong potential to overcome these limitations by leveraging rich contextual understanding. However, their practical use faces two major challenges: outdated service facts and invalid or redundant services. To address these issues, we propose EVOREC, an evolution-aware framework for service recommendation that leverages model editing in a locate-then-edit paradigm to incorporate updated service facts without costly retraining efficiently. This allows the model to remain aligned with evolving service ecosystems. To address invalid service issues, we introduce a Finite Automata (FA)-based constrained decoding mechanism with deduplication, which enforces structural and semantic validity while eliminating repeated services. Experiments on real-world service datasets demonstrate that our framework consistently outperforms existing baselines, e.g., achieving an average relative improvement of 25.9% in Recall@5. Moreover, under evolving service scenarios, our approach outperforms model fine-tuning approaches by 22.3%, demonstrating strong adaptability to service evolution and providing a practical solution for service recommendation in dynamic ecosystems