2 citations · 3 across the 12 of their papers we have counts for
12 papers
TDDN: Text-aligned Diffused DINO Network for Puzzle Understanding
Harsha Patnala, Debopriyo Banerjee, Ayush Sunil Munot +1
Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, which current Vision Language Models (VLMs) often lack. VLMs built on CLIP-based ViT bac…
MuCRASP: Multimodal Chain-of-thought Reasoning aware Structured Pruning
Aritra Dutta, Somak Aditya
Vision-language models (VLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex multimodal tasks, but their large parameter sizes make deployment expensive. St…
AFGNN: API Misuse Detection using Graph Neural Networks and Clustering
Ponnampalam Pirapuraj, Tamal Mondal, Sharanya Gupta +3
Application Programming Interfaces (APIs) are crucial to software development, enabling integration of existing systems with new applications by reusing tried and tested code, savi…
AD: Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systems
Ishan Sahu, Somnath Hazra, Somak Aditya +1
End-to-end autonomous driving systems have achieved significant progress, yet their adversarial robustness remains largely underexplored. In this work, we conduct a closed-loop eva…
PragWorld: A Benchmark Evaluating LLMs' Local World Model under Minimal Linguistic Alterations and Conversational Dynamics
Sachin Vashistha, Aryan Bibhuti, Atharva Naik +2
Real-world conversations are rich with pragmatic elements, such as entity mentions, references, and implicatures. Understanding such nuances is a requirement for successful natural…
EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos
Sourjyadip Ray, Shubham Sharma, Somak Aditya +1
As digital platforms redefine educational paradigms, ensuring interactivity remains vital for effective learning. This paper explores using Multimodal Large Language Models (MLLMs)…
AURA: Affordance-Understanding and Risk-aware Alignment Technique for Large Language Models
Sayantan Adak, Pratyush Chatterjee, Somnath Banerjee +3
Present day LLMs face the challenge of managing affordance-based safety risks-situations where outputs inadvertently facilitate harmful actions due to overlooked logical implicatio…
REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback
Aniruddha Roy, Pretam Ray, Abhilash Nandy +2
Instruction-based Large Language Models (LLMs) have proven effective in numerous few-shot or zero-shot Natural Language Processing (NLP) tasks. However, creating human-annotated in…
ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments
Sourjyadip Ray, Kushal Gupta, Soumi Kundu +3
The global shortage of healthcare workers has demanded the development of smart healthcare assistants, which can help monitor and alert healthcare workers when necessary. We examin…
MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning
Debrup Das, Debopriyo Banerjee, Somak Aditya +1
Tool-augmented Large Language Models (TALMs) are known to enhance the skillset of large language models (LLMs), thereby, leading to their improved reasoning abilities across many t…
TEXT2AFFORD: Probing Object Affordance Prediction abilities of Language Models solely from Text
Sayantan Adak, Daivik Agrawal, Animesh Mukherjee +1
We investigate the knowledge of object affordances in pre-trained language models (LMs) and pre-trained Vision-Language models (VLMs). A growing body of literature shows that PTLMs…
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs
Haritz Puerto, Martin Tutek, Somak Aditya +2
Reasoning is a fundamental component of language understanding. Recent prompting techniques, such as chain of thought, have consistently improved LLMs' performance on various reaso…