4 papers
To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling
Qinyuan Wu, Soumi Das, Mahsa Amani +5
Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities but potentially incurring substantial costs. Moreover, tool use is not always beneficial: r…
Efficient Continual Pre-training of LLMs for Low-resource Languages
Arijit Nag, Soumen Chakrabarti, Animesh Mukherjee +1
Open-source Large Language models (OsLLMs) propel the democratization of natural language research by giving the flexibility to augment or update model parameters for performance i…
Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs
Arijit Nag, Animesh Mukherjee, Niloy Ganguly +1
Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages (HRLs). A few of them have been trained on low-resource l…
A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification
Arijit Nag, Bidisha Samanta, Animesh Mukherjee +2
Relation classification (sometimes called 'extraction') requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challengi…