A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements
📰 ArXiv cs.AI
Learn to detect and resolve pragmatic ambiguities in natural language requirements using a retrieval-augmented framework, improving software development communication
Action Steps
- Apply natural language processing techniques to identify potential pragmatic ambiguities in requirements
- Configure a retrieval-augmented framework to detect ambiguities
- Test the framework using a dataset of natural language requirements
- Compare the results with traditional ambiguity detection methods
- Refine the framework based on the results to improve accuracy
Who Needs to Know This
Software developers, product managers, and stakeholders can benefit from this framework to clarify natural language requirements and avoid misinterpretation
Key Insight
💡 Pragmatic ambiguities in natural language requirements can be detected and resolved using a retrieval-augmented framework, improving software development communication
Share This
💡 Detect & resolve pragmatic ambiguities in natural language requirements with a retrieval-augmented framework! 🤖
Key Takeaways
Learn to detect and resolve pragmatic ambiguities in natural language requirements using a retrieval-augmented framework, improving software development communication
Full Article
Title: A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements
Abstract:
arXiv:2607.04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development. However, the inherent ambiguity in NLRs can pose significant challenges. In particular, some requirements may be misinterpreted due to varying contextual knowledge and domain-specific expectations of the stakeholders, a phenomenon known as pragmatic ambiguity. This paper presents an approach for detecting and reso
Abstract:
arXiv:2607.04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development. However, the inherent ambiguity in NLRs can pose significant challenges. In particular, some requirements may be misinterpreted due to varying contextual knowledge and domain-specific expectations of the stakeholders, a phenomenon known as pragmatic ambiguity. This paper presents an approach for detecting and reso
DeepCamp AI