1 citations · 1 across the 6 of their papers we have counts for
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When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use
Siddharth Chauhan, Thomas Butler, Abhishek Singhania +2
The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings. A common failure occurs when a model selects the correct tool but generates argum…
Zero-Shot Cross-Lingual Transfer using Prefix-Based Adaptation
Snegha A, Sayambhu Sen, Piyush Singh Pasi +2
With the release of new large language models (LLMs) like Llama and Mistral, zero-shot cross-lingual transfer has become increasingly feasible due to their multilingual pretraining…
Multi-lingual Multi-turn Automated Red Teaming for LLMs
Abhishek Singhania, Christophe Dupuy, Shivam Mangale +1
Language Model Models (LLMs) have improved dramatically in the past few years, increasing their adoption and the scope of their capabilities over time. A significant amount of work…
Language-specific Neurons Do Not Facilitate Cross-Lingual Transfer
Soumen Kumar Mondal, Sayambhu Sen, Abhishek Singhania +1
Multilingual large language models (LLMs) aim towards robust natural language understanding across diverse languages, yet their performance significantly degrades on low-resource l…
LoFTI: Localization and Factuality Transfer to Indian Locales
Sona Elza Simon, Soumen Kumar Mondal, Abhishek Singhania +2
Large language models (LLMs) encode vast amounts of world knowledge acquired via training on large web-scale datasets crawled from the internet. However, these datasets typically e…