RIRAG: Regulatory Information Retrieval and Answer Generation

• URL: http://arxiv.org/abs/2409.05677v2
• Date: Mon, 02 Dec 2024 18:13:28 GMT
• Title: RIRAG: Regulatory Information Retrieval and Answer Generation
• Authors: Tuba Gokhan, Kexin Wang, Iryna Gurevych, Ted Briscoe,
• Abstract summary: We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages. We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents. We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric.
• Score: 51.998738311700095
• License:
• Abstract: Regulatory documents, issued by governmental regulatory bodies, establish rules, guidelines, and standards that organizations must adhere to for legal compliance. These documents, characterized by their length, complexity and frequent updates, are challenging to interpret, requiring significant allocation of time and expertise on the part of organizations to ensure ongoing compliance. Regulatory Natural Language Processing (RegNLP) is a multidisciplinary field aimed at simplifying access to and interpretation of regulatory rules and obligations. We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages, facilitating the development of regulatory question-answering systems. We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents, design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric that tests whether generated answers accurately capture all relevant obligations while avoiding contradictions.

Related papers

• 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)
• Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM [11.725133614445093]
  This work presents an interpretable decision-making framework for autonomous vehicles. We develop a Traffic Regulation Retrieval (TRR) Agent based on Retrieval-Augmented Generation (RAG) Given the semantic complexity of the retrieved rules, we also design a reasoning module powered by a Large Language Model (LLM)
  arXiv  Detail & Related papers  (2024-10-07T05:27:22Z)
• The Artificial Intelligence Act: critical overview [0.0]
  This article provides a critical overview of the recently approved Artificial Intelligence Act. It starts by presenting the main structure, objectives, and approach of Regulation (EU) 2024/1689. The text concludes that even if the overall framework can be deemed adequate and balanced, the approach is so complex that it risks defeating its own purpose.
  arXiv  Detail & Related papers  (2024-08-30T21:38:02Z)
• An Open Knowledge Graph-Based Approach for Mapping Concepts and Requirements between the EU AI Act and International Standards [1.9142148274342772]
  The EU's AI Act will shift the focus of such organizations toward conformance with the technical requirements for regulatory compliance. This paper offers a simple and repeatable mechanism for mapping the terms and requirements relevant to normative statements in regulations and standards.
  arXiv  Detail & Related papers  (2024-08-21T18:21:09Z)
• AuditNet: A Conversational AI-based Security Assistant [DEMO] [10.941722434218262]
  We propose a versatile conversational AI assistant framework designed to facilitate compliance checking on the go. Our framework automates the review, indexing, and retrieval of relevant, context-aware information. This AI assistant not only reduces the manual effort involved in compliance checks but also enhances accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-07-19T08:33:07Z)
• Measuring Retrieval Complexity in Question Answering Systems [64.74106622822424]
  Retrieval complexity (RC) is a novel metric conditioned on the completeness of retrieved documents. We propose an unsupervised pipeline to measure RC given an arbitrary retrieval system. Our system can have a major impact on retrieval-based systems.
  arXiv  Detail & Related papers  (2024-06-05T19:30:52Z)
• Legal Requirements Analysis [2.3349787245442966]
  We explore a variety of methods for analyzing legal requirements and exemplify them on representations. We describe possible alternatives for creating machine-analyzable representations from regulations.
  arXiv  Detail & Related papers  (2023-11-23T09:31:57Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• Tag-based regulation of modules in genetic programming improves context-dependent problem solving [62.997667081978825]
  We introduce and experimentally demonstrate tag-based genetic regulation. Tag-based genetic regulation extends existing tag-based naming schemes. We find that tag-based regulation improves problem-solving performance on context-dependent problems.
  arXiv  Detail & Related papers  (2020-12-16T19:49:28Z)
• Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation [49.671882751569534]
  We develop SynQG, a set of transparent syntactic rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules.
  arXiv  Detail & Related papers  (2020-04-18T19:57:39Z)

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.