46 citations · 62 across the 23 of their papers we have counts for
24 papers
Exploring the Role of Diversity in Example Selection for In-Context Learning
Janak Kapuriya, Manit Kaushik, Debasis Ganguly +1
In-Context Learning (ICL) has gained prominence due to its ability to perform tasks without requiring extensive training data and its robustness to noisy labels. A typical ICL work…
POSIX: A Prompt Sensitivity Index For Large Language Models
Anwoy Chatterjee, H S V N S Kowndinya Renduchintala, Sumit Bhatia +1
Despite their remarkable capabilities, Large Language Models (LLMs) are found to be surprisingly sensitive to minor variations in prompts, often generating significantly divergent…
xLP: Explainable Link Prediction for Master Data Management
Balaji Ganesan, Matheen Ahmed Pasha, Srinivasa Parkala +6
Explaining neural model predictions to users requires creativity. Especially in enterprise applications, where there are costs associated with users' time, and their trust in the m…
SMART: Submodular Data Mixture Strategy for Instruction Tuning
H S V N S Kowndinya Renduchintala, Sumit Bhatia, Ganesh Ramakrishnan
Instruction Tuning involves finetuning a language model on a collection of instruction-formatted datasets in order to enhance the generalizability of the model to unseen tasks. Stu…
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
Pragyan Banerjee, Abhinav Java, Surgan Jandial +4
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks. Re…
Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges
Gunjan Singh, Sumit Bhatia, Raghava Mutharaju
Ontologies are used in various domains, with RDF and OWL being prominent standards for ontology development. RDF is favored for its simplicity and flexibility, while OWL enables de…