5 citations · 10 across the 10 of their papers we have counts for
13 papers
DesignSense: A Human Preference Dataset and Reward Modeling Framework for Graphic Layout Generation
Varun Gopal, Rishabh Jain, Aradhya Mathur +6
Graphic layouts serve as an important and engaging medium for visual communication across different channels. While recent layout generation models have demonstrated impressive cap…
AFRAgent : An Adaptive Feature Renormalization Based High Resolution Aware GUI agent
Neeraj Anand, Rishabh Jain, Sohan Patnaik +2
There is a growing demand for mobile user interface (UI) automation, driven by its broad applications across industries. With the advent of visual language models (VLMs), GUI autom…
Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning
Sohan Patnaik, Milan Aggarwal, Sumit Bhatia +1
LLMssuch as GPT-4 have shown a remarkable ability to solve complex questions by generating step-by-step rationales. Prior works have utilized this capability to improve smaller and…
It Helps to Take a Second Opinion: Teaching Smaller LLMs to Deliberate Mutually via Selective Rationale Optimisation
Sohan Patnaik, Milan Aggarwal, Sumit Bhatia +1
Very large language models (LLMs) such as GPT-4 have shown the ability to handle complex tasks by generating and self-refining step-by-step rationales. Smaller language models (SLM…
AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models
Sohan Patnaik, Rishabh Jain, Balaji Krishnamurthy +1
Visual layouts are essential in graphic design fields such as advertising, posters, and web interfaces. The application of generative models for content-aware layout generation has…
SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models
Manav Nitin Kapadnis, Sohan Patnaik, Abhilash Nandy +3
Radiology Report Generation (R2Gen) demonstrates how Multi-modal Large Language Models (MLLMs) can automate the creation of accurate and coherent radiological reports. Existing met…
CABINET: Content Relevance based Noise Reduction for Table Question Answering
Sohan Patnaik, Heril Changwal, Milan Aggarwal +3
Table understanding capability of Large Language Models (LLMs) has been extensively studied through the task of question-answering (QA) over tables. Typically, only a small part of…
SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing imagery using HRNet with Separable Convolution
Priyanka Goyal, Sohan Patnaik, Adway Mitra +1
The accurate mapping of crop production is crucial for ensuring food security, effective resource management, and sustainable agricultural practices. One way to achieve this is by…
: Domain-Specific Fast Continual Pre-training Technique using Document-Level Metadata and Taxonomy
Abhilash Nandy, Manav Nitin Kapadnis, Sohan Patnaik +3
In this paper, we propose (Fast Continual Pre-training Technique using Document Level Metadata and Taxonomy), a novel, compute-efficient framework that utilizes Document…
An Evaluation Framework for Legal Document Summarization
Ankan Mullick, Abhilash Nandy, Manav Nitin Kapadnis +3
A law practitioner has to go through numerous lengthy legal case proceedings for their practices of various categories, such as land dispute, corruption, etc. Hence, it is importan…
Fine-grained Intent Classification in the Legal Domain
Ankan Mullick, Abhilash Nandy, Manav Nitin Kapadnis +2
A law practitioner has to go through a lot of long legal case proceedings. To understand the motivation behind the actions of different parties/individuals in a legal case, it is e…
Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matching
Manav Nitin Kapadnis, Sohan Patnaik, Siba Smarak Panigrahi +2
We present the system description for our submission towards the Key Point Analysis Shared Task at ArgMining 2021. Track 1 of the shared task requires participants to develop metho…
A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks
Shubham Ekapure, Nuruddin Jiruwala, Sohan Patnaik +1
In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model. The goal of the paper is to apprehen…