Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems

• URL: http://arxiv.org/abs/2503.00600v2
• Date: Mon, 02 Jun 2025 02:31:38 GMT
• Title: Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems
• Authors: Alexander W. Lee, Justin Chan, Michael Fu, Nicolas Kim, Akshay Mehta, Deepti Raghavan, Ugur Cetintemel,
• Abstract summary: We introduce semantic integrity constraints (SICs) for specifying and enforcing correctness conditions over LLM outputs in semantic queries.<n>SICs generalize traditional database integrity constraints to semantic settings, supporting common types of constraints, such as grounding, soundness, and exclusion.<n>We present a system design for integrating SICs into query planning and runtime and discuss its realization in AI-augmented DPSs.
• Score: 39.23499993745249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-augmented data processing systems (DPSs) integrate large language models (LLMs) into query pipelines, allowing powerful semantic operations on structured and unstructured data. However, the reliability (a.k.a. trust) of these systems is fundamentally challenged by the potential for LLMs to produce errors, limiting their adoption in critical domains. To help address this reliability bottleneck, we introduce semantic integrity constraints (SICs) -- a declarative abstraction for specifying and enforcing correctness conditions over LLM outputs in semantic queries. SICs generalize traditional database integrity constraints to semantic settings, supporting common types of constraints, such as grounding, soundness, and exclusion, with both proactive and reactive enforcement strategies. We argue that SICs provide a foundation for building reliable and auditable AI-augmented data systems. Specifically, we present a system design for integrating SICs into query planning and runtime execution and discuss its realization in AI-augmented DPSs. To guide and evaluate the vision, we outline several design goals -- covering criteria around expressiveness, runtime semantics, integration, performance, and enterprise-scale applicability -- and discuss how our framework addresses each, along with open research challenges.

Related papers

• Federated In-Context Learning: Iterative Refinement for Improved Answer Quality [62.72381208029899]
  In-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the input.<n>We propose Federated In-Context Learning (Fed-ICL), a general framework that enhances ICL through an iterative, collaborative process.<n>Fed-ICL progressively refines responses by leveraging multi-round interactions between clients and a central server, improving answer quality without the need to transmit model parameters.
  arXiv  Detail & Related papers  (2025-06-09T05:33:28Z)
• Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs [1.7723990552388873]
  Zero-Knowledge Proofs (ZKPs) offer a cryptographic solution that enables provers to demonstrate, through verified computations, adherence to set requirements without revealing sensitive model details or data.<n>We identify five key properties (non-interactivity, transparent setup, standard representations, succinctness, and post-quantum security) critical for their application in AI validation and verification pipelines.
  arXiv  Detail & Related papers  (2025-05-26T15:39:11Z)
• RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification [10.378433440829712]
  We propose a fully automated framework, RedactOR for de-identifying structured and unstructured electronic health records.<n>Our framework employs cost-efficient De-ID strategies, including intelligent routing, hybrid rule and LLM based approaches.<n>We present a retrieval-based entity relexicalization approach to ensure consistent substitutions of protected entities.
  arXiv  Detail & Related papers  (2025-05-23T21:13:18Z)
• Evaluating Large Language Models for Real-World Engineering Tasks [75.97299249823972]
  This paper introduces a curated database comprising over 100 questions derived from authentic, production-oriented engineering scenarios.<n>Using this dataset, we evaluate four state-of-the-art Large Language Models (LLMs)<n>Our results show that LLMs demonstrate strengths in basic temporal and structural reasoning but struggle significantly with abstract reasoning, formal modeling, and context-sensitive engineering logic.
  arXiv  Detail & Related papers  (2025-05-12T14:05:23Z)
• Structured Prompting and Feedback-Guided Reasoning with LLMs for Data Interpretation [0.0]
  Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and task generalization.<n>This paper introduces the STROT Framework, a method for structured prompting and feedback-driven transformation logic generation.
  arXiv  Detail & Related papers  (2025-05-03T00:05:01Z)
• CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass [3.0566617373924325]
  Recent advances in pre-trained language models (PLMs) have driven remarkable progress in this field. We propose CSE-SFP, an innovative method that exploits the structural characteristics of generative models. We show that CSE-SFP not only produces higher-quality embeddings but also significantly reduces both training time and memory consumption.
  arXiv  Detail & Related papers  (2025-05-01T08:27:14Z)
• Computational Reasoning of Large Language Models [51.629694188014064]
  We introduce textbfTuring Machine Bench, a benchmark to assess the ability of Large Language Models (LLMs) to execute reasoning processes.<n> TMBench incorporates four key features: self-contained and knowledge-agnostic reasoning, a minimalistic multi-step structure, controllable difficulty, and a theoretical foundation based on Turing machine.
  arXiv  Detail & Related papers  (2025-04-29T13:52:47Z)
• Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases [0.0]
  This paper presents a Small Language Model(SLM)-driven system that synergizes advancements in lightweight Retrieval-Augmented Generation (RAG) and semantic-aware data structuring.<n>By integrating MiniRAG's semantic-aware heterogeneous graph indexing and topology-enhanced retrieval with SLM-powered structured data extraction, our system addresses the limitations of traditional methods.<n> Experimental results demonstrate superior performance in accuracy and efficiency, while the introduction of semantic entropy as an unsupervised evaluation metric provides robust insights into model uncertainty.
  arXiv  Detail & Related papers  (2025-04-08T03:28:03Z)
• GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics [9.549568621873386]
  GateLens is an LLM-based system for analyzing data in the automotive domain.<n>Unlike traditional multi-agent or planning-based systems that can be slow, opaque, and costly to maintain, GateLens emphasizes speed, transparency, and reliability.
  arXiv  Detail & Related papers  (2025-03-27T17:48:32Z)
• Towards more Contextual Agents: An extractor-Generator Optimization Framework [0.0]
  Large Language Model (LLM)-based agents have demonstrated remarkable success in solving complex tasks across a wide range of general-purpose applications.<n>However, their performance often degrades in context-specific scenarios, such as specialized industries or research domains.<n>To address this challenge, our work introduces a systematic approach to enhance the contextual adaptability of LLM-based agents.
  arXiv  Detail & Related papers  (2025-02-18T15:07:06Z)
• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge. Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions [59.5243730853157]
  Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets.<n>This article conducts a comparative analysis of three advanced federated LLM (FedLLM) frameworks that integrate knowledge distillation (KD) and split learning (SL) to mitigate these issues.
  arXiv  Detail & Related papers  (2025-01-08T11:37:06Z)
• Optimizing Large Language Models for Dynamic Constraints through Human-in-the-Loop Discriminators [0.0]
  Large Language Models (LLMs) have recently demonstrated impressive capabilities across various real-world applications. We propose a flexible framework that enables LLMs to interact with system interfaces, summarize constraint concepts, and continually optimize performance metrics. Our framework achieved a $7.78%$ pass rate with the human discriminator and a $6.11%$ pass rate with the LLM-based discriminator.
  arXiv  Detail & Related papers  (2024-10-19T17:27:38Z)
• Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification [76.14641982122696]
  We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. We show that our approach leads to an LLM that produces fewer inappropriate responses while achieving competitive performance on benchmarks and a toxicity detection task.
  arXiv  Detail & Related papers  (2024-10-07T23:38:58Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content [62.685566387625975]
  Current mitigation strategies, while effective, are not resilient under adversarial attacks. This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently moderate harmful and unsafe inputs.
  arXiv  Detail & Related papers  (2024-03-19T07:25:02Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing [15.957744324299869]
  In this work, we measure, analyze and mitigate constraints violations in task-oriented semantic parsing. We investigate two mitigation strategies: Semantic-Retrieval of Demonstrations (SRD) and API-aware Constrained Decoding (API-CD) Our experiments show that these strategies are effective at reducing constraints violations and improving the quality of the generated API calls, but require careful consideration given their implementation complexity and latency.
  arXiv  Detail & Related papers  (2023-05-24T16:50:36Z)
• Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice [62.44110411199835]
  We build on literature in machine learning law to propose framework for limiting collection based on data interpretation that ties data to system performance. We formalize a data minimization criterion based on performance curve derivatives and provide an effective and interpretable piecewise power law technique.
  arXiv  Detail & Related papers  (2021-07-16T19:59:01Z)
• Teaching the Old Dog New Tricks: Supervised Learning with Constraints [18.88930622054883]
  Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems. Existing approaches typically apply constrained optimization techniques to ML training, enforce constraint satisfaction by adjusting the model design, or use constraints to correct the output. Here, we investigate a different, complementary, strategy based on "teaching" constraint satisfaction to a supervised ML method via the direct use of a state-of-the-art constraint solver.
  arXiv  Detail & Related papers  (2020-02-25T09:47:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.