1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
Grading video interviews with fairness considerations
Abhishek Singhania, Abhishek Unnam, Varun Aggarwal
There has been considerable interest in predicting human emotions and traits using facial images and videos. Lately, such work has come under criticism for poor labeling practices,…