6 papers
Evaluating Cross-lingual Knowledge Consistency in Code-Mixed vis-a-vis Indian Languages using IndicKLAR
Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali +3
Large language models recall knowledge reliably in English but often fail on the same query posed in a lower-resourced language -- a crosslingual consistency gap that remains under…
DEPART: DEcomposing PARiTy across Multilingual LLMs
Manan Uppadhyay, Prashant Kodali, Pranjal Chitale +3
Multilingual Large Language Models (mLLMs) leaderboards report per-language accuracy but rarely explain why disparities emerge, leaving systemic biases unattributed and offering pr…
Neither Here Nor There: Cross-Lingual Representation Dynamics of Code-Mixed Text in Multilingual Encoders
Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali +1
Multilingual encoder-based language models are widely adopted for code-mixed analysis tasks, yet we know surprisingly little about how they represent code-mixed inputs internally -…
UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages
Pranjal A. Chitale, Varun Gumma, Sanchit Ahuja +4
Developing culturally grounded multilingual AI systems remains challenging, particularly for low-resource languages. While synthetic data offers promise, its effectiveness in multi…
Adapting Multilingual Models to Code-Mixed Tasks via Model Merging
Prashant Kodali, Vaishnavi Shivkumar, Swarang Joshi +3
We study model merging as a practical alternative to conventional adaptation strategies for code-mixed NLP. Starting from a multilingual base model, we: (i) perform continued pre-t…
From Human Judgements to Predictive Models: Unravelling Acceptability in Code-Mixed Sentences
Prashant Kodali, Anmol Goel, Likhith Asapu +5
Current computational approaches for analysing or generating code-mixed sentences do not explicitly model ``naturalness'' or ``acceptability'' of code-mixed sentences, but rely on…