4 papers
PRISM: Demystifying Retention and Interaction in Mid-Training
Bharat Runwal, Ashish Agrawal, Anurag Roy +1
We present PRISM, a comprehensive empirical study of mid-training design choices for large language models. Through controlled experiments across seven base models spanning four fa…
TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments
Zhangchen Xu, Adriana Meza Soria, Shawn Tan +4
Large Language Model (LLM) agents are rapidly emerging as powerful systems for automating tasks across domains. Yet progress in the open-source community is constrained by the lack…
Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data Selection
Barah Fazili, Ashish Sunil Agrawal, Preethi Jyothi
Large language models (LLMs) are very proficient text generators. We leverage this capability of LLMs to generate task-specific data via zero-shot prompting and promote cross-lingu…
Translation Errors Significantly Impact Low-Resource Languages in Cross-Lingual Learning
Ashish Sunil Agrawal, Barah Fazili, Preethi Jyothi
Popular benchmarks (e.g., XNLI) used to evaluate cross-lingual language understanding consist of parallel versions of English evaluation sets in multiple target languages created w…