7 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…
: 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…