Language Identification (LI) has traditionally been performed at the document or sentence level and specifically for high-resource languages. The rise of social media communication in multilingual regions such as India has seen users generate code-mixed texts, which typically combine English with local languages. Here, word-level LI is of primary importance: a system assigns a language label to each word in the text. It provides a fine granularity essential for capturing frequent language switches in informal, transliterated content. Indeed, it becomes an important step in most downstream NLP tasks such as machine translation, sentiment analysis, and conversation systems. This becomes all the more important because many languages of the Dravidian family are spoken by millions but are characteristically low-resourced. The CoLI-Dravidian shared task attempts to fill this gap by taking up word-level LI in Roman-script code-mixed Dravidian datasets. A total of eight teams participated in this shared task, and the top models achieved macro F1 scores 0.743, 0.921, 0.827, 0.952, and 0.823 for Tamil, Kannada, Malayalam, Telugu, and Tulu, respectively, indicating both the complexity and the progress in this domain.
