Script Gap: Evaluating LLM Triage on Indian Languages in Native vs Roman Scripts in a Real World Setting

ScoutEdge, an AI-driven sports tech startup, is transforming athlete scouting in India by utilizing advanced technology to identify and promote underrated talent, thereby democratizing access to opportunities in sports. This innovation aims to bridge the gap between aspiring athletes and professional platforms.

Meta has appointed Aman Jain to lead its public policy efforts in India, bringing over two decades of experience in public policy and business strategy to the role. This strategic move is aimed at enhancing Meta's engagement with Indian regulatory frameworks and stakeholders.

In 2026, India is poised to make significant advancements in artificial intelligence (AI), but experts warn that only resilient players in the sector will thrive amid increasing competition and evolving technologies. The landscape is shifting as Indian IT companies transition from embedded AI solutions to more modular products, enhancing adaptability and operational efficiency.

The newly introduced framework, Text2Graph, integrates lightweight large language models (LLMs) with graph neural networks (GNNs) to enhance text classification, particularly in scenarios with limited labels. This open-source Python package allows for flexible component swapping, including feature extractors and sampling strategies, and has been benchmarked across five datasets for zero-shot classification tasks.

An open, machine-readable dataset of Greek government decisions has been introduced, sourced from the national transparency platform Diavgeia, comprising 1 million decisions with high-quality raw text extracted from PDFs. This dataset is released with a reproducible extraction pipeline and includes qualitative analyses to explore boilerplate patterns and a retrieval-augmented generation (RAG) task to evaluate information access and reasoning over governmental documents.

This chapter discusses the use of Large Language Models (LLMs) in the legal field, highlighting their ability to enhance traditional legal tasks such as interpreting statutes, contracts, and case law. It also addresses the challenges posed by these technologies, including algorithmic monoculture and compliance with regulations like the EU's AI Act and U.S. initiatives.

LMSpell has been introduced as a neural spell checking toolkit specifically designed for low-resource languages (LRLs), showcasing the effectiveness of large language models (LLMs) in improving spell correction. This toolkit includes an evaluation function that addresses the hallucination issues often associated with LLMs, marking a significant advancement in the field of natural language processing for underrepresented languages.

A novel approach to Named Entity Recognition (NER) for Portuguese has been introduced, utilizing a three-step ensemble pipeline of locally run Large Language Models (LLMs). This method demonstrates superior performance over individual models across multiple datasets, particularly in zero-shot scenarios, where minimal annotated data is available.