NIM: Neuro-symbolic Ideographic Metalanguage for Inclusive Communication

TL;DR

The paper addresses the digital divide faced by semi-literate populations by proposing NIM, a neuro-symbolic ideographic metalanguage that blends Natural Semantic Metalanguage with large language models. It implements a two-layer architecture comprising a symbolic NSM-based structure and a neural prompting system, guided by Tree Of Thoughts reasoning, to decompose complex concepts into manageable ideographs and binding text. Through participatory design with over 200 semi-literate participants and multi-faceted evaluation, NIM achieves over 80% semantic comprehensibility and demonstrates strong handling of out-of-vocabulary concepts, with ablation studies highlighting the importance of binding text. The approach is multilingual, domain- and geography-agnostic, and positioned for open-source deployment to foster culturally adaptive, inclusive communication across diverse communities.

Abstract

Digital communication has become the cornerstone of modern interaction, enabling rapid, accessible, and interactive exchanges. However, individuals with lower academic literacy often face significant barriers, exacerbating the "digital divide". In this work, we introduce a novel, universal ideographic metalanguage designed as an innovative communication framework that transcends academic, linguistic, and cultural boundaries. Our approach leverages principles of Neuro-symbolic AI, combining neural-based large language models (LLMs) enriched with world knowledge and symbolic knowledge heuristics grounded in the linguistic theory of Natural Semantic Metalanguage (NSM). This enables the semantic decomposition of complex ideas into simpler, atomic concepts. Adopting a human-centric, collaborative methodology, we engaged over 200 semi-literate participants in defining the problem, selecting ideographs, and validating the system. With over 80\% semantic comprehensibility, an accessible learning curve, and universal adaptability, our system effectively serves underprivileged populations with limited formal education.

Figures (9)

• Figure 1: High Level NIM architecture, comprising of Neuro-symbolic AI, Pipeline and Orchestration layers.
• Figure 2: Sample output (binding text in Marathi).
• Figure 3: Sample output (binding text in English).
• Figure 4: Growth of picturable words with exploration of dataset.
• Figure :