一种用于检测和解决自然语言需求中语用歧义的检索增强框架
A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements
AI总结：
研究自然语言需求中的语用歧义，利用检索增强技术和不同领域知识库模拟不同专业利益相关者，生成候选消歧需求并验证，评估该方法在需求文档上的效果。
摘要：
自然语言需求对弥合软件开发中不同利益相关者的沟通差距至关重要。然而，其内在歧义会带来挑战，即语用歧义。本文提出一种检测和解决自然语言需求中语用歧义的方法。该方法利用检索增强生成技术和不同领域知识库来检测需求解释差异，生成候选消歧需求并由分析师验证。通过在两个需求规范文档上评估，结果表明该方法可行。
英文摘要：
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 resolving pragmatic ambiguities in NLRs. The approach leverages retrieval-augmented generation techniques with novice, intermediate, and expert domain knowledge bases to simulate stakeholders with varying domain expertise and detect discrepancies in requirement interpretation. Candidate disambiguated requirements are generated using the expert domain knowledge base, with final validation by a requirements analyst required to ensure alignment with the intended functionality. We evaluate the approach on two requirements specification documents from the PUblic REquirements dataset, using four large language models: GPT-4o-mini, Mistral-7B, Llama-3.1-8B, and Qwen2.5-7B. Detection performance is assessed using macro-averaged accuracy, precision, recall, F1, and F2 scores. The resolution quality of the candidate disambiguated requirements is measured through human evaluation of relevance, clarity, and consistency. In this initial evaluation, results show that the proposed approach can detect pragmatic ambiguities and produce candidate disambiguated requirements that are relevant, clear, and consistent with the intended system functionality. Among the evaluated models, GPT-4o-mini achieved the highest macro-averaged recall (0.75) and F2 score (0.75) for pragmatic ambiguity detection. In the resolution task, GPT-4o-mini received the highest relevance scores from human evaluators, while Mistral-7B achieved the highest scores for clarity and consistency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
