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20242026
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cs.CL2026

The Percept-V Challenge: Can Multimodal LLMs Crack Simple Perception Problems?

Samrajnee Ghosh, Naman Agarwal, Hemanshu Garg +3

Cognitive science research treats visual perception, the ability to understand and make sense of a visual input, as one of the early developmental signs of intelligence. Its TVPS-4…

cs.CL2024

RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions

Prayushi Faldu, Indrajit Bhattacharya, Mausam

An essential requirement for a real-world Knowledge Base Question Answering (KBQA) system is the ability to detect the answerability of questions when generating logical forms. How…

cs.CL2024

DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion

Ananjan Nandi, Navdeep Kaur, Parag Singla +1

We consider two popular approaches to Knowledge Graph Completion (KGC): textual models that rely on textual entity descriptions, and structure-based models that exploit the connect…

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.CL2024

Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning

Mayur Patidar, Riya Sawhney, Avinash Singh +3

Existing Knowledge Base Question Answering (KBQA) architectures are hungry for annotated data, which make them costly and time-consuming to deploy. We introduce the problem of few-…