18 citations · 21 across the 23 of their papers we have counts for
24 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…
IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correction
Ankan Mullick, Sukannya Purkayastha, Saransh Sharma +2
Voice-controlled dialog systems have become immensely popular due to their ability to perform a wide range of actions in response to diverse user queries. These agents possess a pr…
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…
Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection
Ankan Mullick, Saransh Sharma, Abhik Jana +1
The rise of multimodal data, integrating text, audio, and visuals, has created new opportunities for studying multimodal tasks such as intent detection. This work investigates the…
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…
A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents
Ankan Mullick, Sombit Bose, Abhilash Nandy +2
In task-oriented dialogue systems, intent detection is crucial for interpreting user queries and providing appropriate responses. Existing research primarily addresses simple queri…
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…
Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling
Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh +5
We study the problem of automatically annotating relevant numerals (GAAP metrics) occurring in the financial documents with their corresponding XBRL tags. Different from prior work…
Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts
Subhendu Khatuya, Koushiki Sinha, Niloy Ganguly +2
While automatic summarization techniques have made significant advancements, their primary focus has been on summarizing short news articles or documents that have clear structural…
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…
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…
Intent Detection and Entity Extraction from BioMedical Literature
Ankan Mullick, Mukur Gupta, Pawan Goyal
Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature. Despite recent research on large-language…
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…
MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction
Ankan Mullick, Akash Ghosh, G Sai Chaitanya +5
Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entit…
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction
Rajdeep Mukherjee, Nithish Kannen, Saurabh Kumar Pandey +1
Existing works on Aspect Sentiment Triplet Extraction (ASTE) explicitly focus on developing more efficient fine-tuning techniques for the task. Instead, our motivation is to come u…
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…