Accessing government welfare schemes in India remains difficult for emergent users—individuals with limited literacy, digital familiarity, or language support. This paper compares two mobile platforms that deliver the same scheme-related information but differ in interaction modality: myScheme, a government-built, form-based Android application, and Prabodhini, a voice-based conversational prototype powered by generative AI and Retrieval-Augmented Generation (RAG). Through a task-based comparative study with 15 low-income participants, we examine usability, task completion time, and user preference. Drawing on theories such as the Gulf of Execution and Zipf’s Law of Least Effort, we show that Prabodhini’s conversational design and support for natural language input better align with emergent users’ mental models and practices. Our findings highlight the value of multimodal, voice-first NLP systems for improving trust, access, and inclusion in public digital services. We discuss implications for designing accessible language technologies for marginalised populations.

Constructing legal knowledge graphs from unstructured legal texts is a complex challenge due to the intricate nature of legal language. While open information extraction (OIE) techniques can convert text into triples of the form subject, relation, object, they often fall short of capturing the nuanced relationships within lengthy legal sentences, necessitating more sophisticated approaches known as complex information extraction. This paper proposes LeGen – an end-to-end approach leveraging pre-trained large language models (GPT-4o, T5, BART) to perform complex information extraction from legal sentences. LeGen learns and represents the discourse structure of legal sentences, capturing both their complexity and semantics. It minimizes error propagation typical in multi-step pipelines and achieves up to a 32.2% gain on the Indian Legal benchmark. Additionally, it demonstrates competitive performance on open information extraction benchmarks. A promising application of the resulting legal knowledge graphs is in developing question-answering systems for government schemes, tailored to the Next Billion Users who struggle with the complexity of legal language. Our code and data are available at https://github.com/prajnaupadhyay/LegalIE