Pavithra PM Nair

Pavithra PM Nair, Preethu Rose Anish

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.

Pavithra PM Nair, Preethu Rose Anish

In jurisdictions like India, where courts face an extensive backlog of cases, artificial intelligence offers transformative potential for legal judgment prediction. A critical subset of this backlog comprises appellate cases, which are formal decisions issued by higher courts reviewing the rulings of lower courts. To this end, we present Vichara, a novel framework tailored to the Indian judicial system that predicts and explains appellate judgments. Vichara processes English-language appellate case proceeding documents and decomposes them into decision points. Decision points are discrete legal determinations that encapsulate the legal issue, deciding authority, outcome, reasoning, and temporal context. The structured representation isolates the core determinations and their context, enabling accurate predictions and interpretable explanations. Vichara's explanations follow a structured format inspired by the IRAC (Issue-Rule-Application-Conclusion) framework and adapted for Indian legal reasoning. This enhances interpretability, allowing legal professionals to assess the soundness of predictions efficiently. We evaluate Vichara on two datasets, PredEx and the expert-annotated subset of the Indian Legal Documents Corpus (ILDC_expert), using four large language models: GPT-4o mini, Llama-3.1-8B, Mistral-7B, and Qwen2.5-7B. Vichara surpasses existing judgment prediction benchmarks on both datasets, with GPT-4o mini achieving the highest performance (F1: 81.5 on PredEx, 80.3 on ILDC_expert), followed by Llama-3.1-8B. Human evaluation of the generated explanations across Clarity, Linking, and Usefulness metrics highlights GPT-4o mini's superior interpretability.