1 citations · 1 across the 16 of their papers we have counts for
16 papers
CaRGo-T: Causal Reasoning Graph-of-Thought improves Multimodal Humor Comprehension
Abhilash Nandy, Rahul Seetharaman, Aman Bansal +5
Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humor remains challenging because…
Model Agnostic Graph Prompt Learning for Crystal Property Prediction
Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2
Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties. These models often encode domain-specific knowledge into t…
Latent Diffusion Pretraining for Crystal Property Prediction
Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2
Fast and accurate prediction of crystal properties is a central challenge in new materials design. Graph neural networks and Transformer-based models have emerged as powerful tools…
LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation
Subhojyoti Khastagir, Kishalay Das, Pawan Goyal +3
Recent advances in generative modeling have shown significant promise in designing novel periodic crystal structures. Existing approaches typically rely on either large language mo…
Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents
Ankan Mullick, Sombit Bose, Rounak Saha +6
In this paper, we introduce Spotlight, a novel paradigm for information extraction that produces concise, engaging narratives by highlighting the most compelling aspects of a docum…
Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval
Subhendu Khatuya, Shashwat Naidu, Pawan Goyal +1
Despite continuous advancements in the capabilities of large language models (LLMs), numerical reasoning remains a challenging area. Techniques like chain-of-thought prompting, tre…
Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification
Subhendu Khatuya, Shashwat Naidu, Saptarshi Ghosh +2
The explosion of textual data has made manual document classification increasingly challenging. To address this, we introduce a robust, efficient domain-agnostic generative model f…
Periodic Materials Generation using Text-Guided Joint Diffusion Model
Kishalay Das, Subhojyoti Khastagir, Pawan Goyal +3
Equivariant diffusion models have emerged as the prevailing approach for generating novel crystal materials due to their ability to leverage the physical symmetries of periodic mat…
YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models
Abhilash Nandy, Yash Agarwal, Ashish Patwa +5
Understanding satire and humor is a challenging task for even current Vision-Language models. In this paper, we propose the challenging tasks of Satirical Image Detection (detectin…
Leveraging the Power of LLMs: A Fine-Tuning Approach for High-Quality Aspect-Based Summarization
Ankan Mullick, Sombit Bose, Rounak Saha +6
The ever-increasing volume of digital information necessitates efficient methods for users to extract key insights from lengthy documents. Aspect-based summarization offers a targe…
On The Persona-based Summarization of Domain-Specific Documents
Ankan Mullick, Sombit Bose, Rounak Saha +5
In an ever-expanding world of domain-specific knowledge, the increasing complexity of consuming, and storing information necessitates the generation of summaries from large informa…
Order-Based Pre-training Strategies for Procedural Text Understanding
Abhilash Nandy, Yash Kulkarni, Pawan Goyal +1
In this paper, we propose sequence-based pretraining methods to enhance procedural understanding in natural language processing. Procedural text, containing sequential instructions…
How Robust are the Tabular QA Models for Scientific Tables? A Study using Customized Dataset
Akash Ghosh, B Venkata Sahith, Niloy Ganguly +2
Question-answering (QA) on hybrid scientific tabular and textual data deals with scientific information, and relies on complex numerical reasoning. In recent years, while tabular Q…
Long Dialog Summarization: An Analysis
Ankan Mullick, Ayan Kumar Bhowmick, Raghav R +4
Dialog summarization has become increasingly important in managing and comprehending large-scale conversations across various domains. This task presents unique challenges in captu…
CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text
Abhilash Nandy, Manav Nitin Kapadnis, Pawan Goyal +1
In this paper, we propose CLMSM, a domain-specific, continual pre-training framework, that learns from a large set of procedural recipes. CLMSM uses a Multi-Task Learning Framework…
: 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…