activity
20162024
most citedMaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models

3 citations · 10 across the 11 of their papers we have counts for

collaborators

11 papers

cs.AI20243 cited

Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion

Ananjan Nandi, Navdeep Kaur, Parag Singla +1

High-quality and high-coverage rule sets are imperative to the success of Neuro-Symbolic Knowledge Graph Completion (NS-KGC) models, because they form the basis of all symbolic inf…

cs.CL2024

SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models

Vipul Rathore, Aniruddha Deb, Ankish Chandresh +2

Recently, very large language models (LLMs) have shown exceptional performance on several English NLP tasks with just in-context learning (ICL), but their utility in other language…

cs.CL20231 cited

CoRE-CoG: Conversational Recommendation of Entities using Constrained Generation

Harshvardhan Srivastava, Kanav Pruthi, Soumen Chakrabarti +1

End-to-end conversational recommendation systems (CRS) generate responses by leveraging both dialog history and a knowledge base (KB). A CRS mainly faces three key challenges: (1)…

cs.CL2023

ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language Adapters

Vipul Rathore, Rajdeep Dhingra, Parag Singla +1

We tackle the problem of zero-shot cross-lingual transfer in NLP tasks via the use of language adapters (LAs). Most of the earlier works have explored training with adapter of a si…

cs.LG2023

Towards Fair and Calibrated Models

Anand Brahmbhatt, Vipul Rathore, Mausam +1

Recent literature has seen a significant focus on building machine learning models with specific properties such as fairness, i.e., being non-biased with respect to a given set of…

cs.CL20233 cited

MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models

Mohd Zaki, Jayadeva, Mausam +1

Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Langu…