Sheth, RajveeRajveeShethBeniwal, HimanshuHimanshuBeniwalSingh, MayankMayankSingh2026-02-112026-02-112025-11-04[9798891763357]10.18653/v1/2025.findings-emnlp.4222-s2.0-105028991183https://repository.iitgn.ac.in/handle/IITG2025/34605We introduce COMI-LINGUA, the largest manually annotated Hindi-English code-mixed dataset, comprising 125K+ high-quality instances across five core NLP tasks: Token-level Language Identification, Matrix Language Identification, Named Entity Recognition, Part-Of-Speech Tagging and Machine Translation. Each instance is annotated by three bilingual annotators, yielding over 376K expert annotations with strong inter-annotator agreement (Fleiss’ Kappa ≥ 0.81). The rigorously preprocessed and filtered dataset covers both Devanagari and Roman scripts and spans diverse domains, ensuring real-world linguistic coverage. Evaluation reveals that closed-weight LLMs significantly outperform traditional tools and open-weight models in zero-shot settings. Notably, one-shot prompting consistently boosts performance across tasks, especially in structure-sensitive predictions like POS and NER. Fine-tuning open-weight LLMs on COMI-LINGUA demonstrates substantial improvements, achieving up to 95.25 F1 in NER, 98.77 F1 in MLI, and competitive MT performance, setting new benchmarks for Hinglish code-mixed text. COMI-LINGUA is publicly available at this URL¹en-USfalseConference Paperhttps://aclanthology.org/2025.findings-emnlp.422.pdf7973-799220250cpConference Paper