Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance

• URL: http://arxiv.org/abs/2507.16711v1
• Date: Tue, 22 Jul 2025 15:46:44 GMT
• Title: Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance
• Abstract summary: Risk and Quality (R&Q) assurance in highly regulated industries requires constant navigation of complex regulatory frameworks.<n>We present a novel Retrieval Augmented Generation (RAG) system leveraging Large Language Models (LLMs), hybrid search and relevance boosting to enhance R&Q query processing.
• Score: 0.8501032945185112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Risk and Quality (R&Q) assurance in highly regulated industries requires constant navigation of complex regulatory frameworks, with employees handling numerous daily queries demanding accurate policy interpretation. Traditional methods relying on specialized experts create operational bottlenecks and limit scalability. We present a novel Retrieval Augmented Generation (RAG) system leveraging Large Language Models (LLMs), hybrid search and relevance boosting to enhance R&Q query processing. Evaluated on 124 expert-annotated real-world queries, our actively deployed system demonstrates substantial improvements over traditional RAG approaches. Additionally, we perform an extensive hyperparameter analysis to compare and evaluate multiple configuration setups, delivering valuable insights to practitioners.

Related papers

• Deepchecks: Evaluating Retrieval-Augmented Generation (RAG) [4.4767889830241705]
  This paper introduces Deepchecks, a comprehensive framework tailored for evaluating RAG applications.<n>By ensuring alignment with application-specific requirements, Deepchecks framework provides a robust foundation for assessing reliability, relevance, and user satisfaction in RAG systems.
  arXiv  Detail & Related papers  (2026-05-14T07:27:50Z)
• Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models [4.155649113742267]
  ReQUESTA is a hybrid, multi-agent framework for generating cognitively diverse multiple-choice questions (MCQs)<n>We evaluated the framework in a large-scale reading comprehension study using academic expository texts.<n>Results showed that ReQUESTA-generated items were consistently more challenging, more discriminative, and more strongly aligned with overall reading comprehension performance.
  arXiv  Detail & Related papers  (2026-02-03T16:26:47Z)
• Augmenting Question Answering with A Hybrid RAG Approach [10.99985378187219]
  Retrieval-Augmented Generation (RAG) has emerged as a powerful technique for enhancing the quality of responses in Question-Answering (QA) tasks.<n>We introduce Structured-Semantic RAG (SSRAG), a hybrid architecture that enhances QA quality by integrating query augmentation, agentic routing, and a structured retrieval mechanism.
  arXiv  Detail & Related papers  (2026-01-19T02:08:47Z)
• Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation [0.16754194618631593]
  This paper introduces an agentic RAG architecture to address domain-specific and dense terminology challenges.<n>We evaluate our approach against a standard RAG baseline using a curated dataset of 85 question-answer-reference triples from an enterprise knowledge base.
  arXiv  Detail & Related papers  (2025-10-29T13:41:36Z)
• Enhancing Retrieval Augmentation via Adversarial Collaboration [50.117273835877334]
  We propose the Adrial Collaboration RAG (AC-RAG) framework to address "Retrieval Hallucinations"<n>AC-RAG employs two heterogeneous agents: a generalist Detector that identifies knowledge gaps, and a domain-specialized Resolver that provides precise solutions.<n>Experiments show that AC-RAG significantly improves retrieval accuracy and outperforms state-of-the-art RAG methods across various vertical domains.
  arXiv  Detail & Related papers  (2025-09-18T08:54:20Z)
• Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger [51.01841635655944]
  Recent advancements in Large Vision Language Models (LVLMs) have significantly improved performance in Visual Question Answering (VQA) tasks.<n>Existing methods still face challenges, such as the scarcity of knowledge with reasoning examples and erratic responses from retrieved knowledge.<n>We propose a multimodal RAG framework, termed RCTS, which enhances LVLMs by constructing a Reasoning Context-enriched knowledge base and a Tree Search re-ranking method.
  arXiv  Detail & Related papers  (2025-06-09T14:00:57Z)
• Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation [2.4830284216463]
  This paper introduces a novel hybrid framework that integrates RAG with intent-based canned responses.<n>Our framework employs a dialogue context manager to ensure coherence in multi-turn interactions and incorporates a feedback loop to refine intents.<n> Experimental results demonstrate that the proposed framework achieves a balance of high accuracy (95%) and low latency (180ms)
  arXiv  Detail & Related papers  (2025-06-02T17:59:27Z)
• Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation [52.3707788779464]
  We introduce a novel Jensen-Shannon Divergence driven method to Attribute Response to Context (ARC-JSD)<n>ARC-JSD enables efficient and accurate identification of essential context sentences without additional fine-tuning, gradient-calculation or surrogate modelling.<n> Evaluations on a wide range of RAG benchmarks, such as TyDi QA, Hotpot QA, and Musique, using instruction-tuned LLMs in different scales demonstrate superior accuracy and significant computational efficiency improvements.
  arXiv  Detail & Related papers  (2025-05-22T09:04:03Z)
• Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
  Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC)<n>RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks.<n>Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
  arXiv  Detail & Related papers  (2025-02-08T06:50:47Z)
• PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation [16.081923602156337]
  We introduce sPecIalized KnowledgE and Rationale Augmentation Generation (PIKE-RAG)<n>We focus on extracting, understanding, and applying specialized knowledge, while constructing coherent rationale to incrementally steer LLMs toward accurate responses.<n>This strategic approach offers a roadmap for the phased development and enhancement of RAG systems, tailored to meet the evolving demands of industrial applications.
  arXiv  Detail & Related papers  (2025-01-20T15:39:39Z)
• A Survey of Query Optimization in Large Language Models [10.255235456427037]
  RAG mitigates the limitations of Large Language Models by dynamically retrieving and leveraging up-to-date relevant information.<n>QO has emerged as a critical element, playing a pivotal role in determining the effectiveness of RAG's retrieval stage.
  arXiv  Detail & Related papers  (2024-12-23T13:26:04Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively.<n>We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models [26.353428245346166]
  The Extract-Refine-Retrieve-Read (ERRR) framework is designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RAG) systems. Unlike conventional query optimization techniques used in RAG, the ERRR framework begins by extracting knowledge from Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-11-12T14:12:45Z)
• An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms [62.878616839799776]
  We propose SynthRAG, an innovative framework designed to enhance Question Answering (QA) performance. SynthRAG improves on conventional models by employing adaptive outlines for dynamic content structuring. An online deployment on the Zhihu platform revealed that SynthRAG's answers achieved notable user engagement.
  arXiv  Detail & Related papers  (2024-10-23T09:14:57Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations [51.76373105981212]
  Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms.<n>We introduce a comprehensive reranking framework, designed to seamlessly integrate various reranking criteria.<n>A customizable input mechanism is also integrated, enabling the tuning of the language model's focus to meet specific reranking needs.
  arXiv  Detail & Related papers  (2024-06-18T09:29:18Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process [0.0]
  Regulatory compliance in the pharmaceutical industry entails navigating through complex and voluminous guidelines. To address these challenges, our study introduces a generative AI and the Retrieval Augmented Generation (RAG) method. This paper details QA-RAG's structure and performance evaluation, emphasizing its potential for the regulatory compliance domain.
  arXiv  Detail & Related papers  (2024-01-26T08:23:29Z)
• Better Retrieval May Not Lead to Better Question Answering [59.1892787017522]
  A popular approach to improve the system's performance is to improve the quality of the retrieved context from the IR stage. We show that for StrategyQA, a challenging open-domain QA dataset that requires multi-hop reasoning, this common approach is surprisingly ineffective.
  arXiv  Detail & Related papers  (2022-05-07T16:59:38Z)

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.