no code implementations • 21 Apr 2024 • Charith Chandra Sai Balne, Sreyoshi Bhaduri, Tamoghna Roy, Vinija Jain, Aman Chadha
The rise of deep learning has marked significant progress in fields such as computer vision, natural language processing, and medical imaging, primarily through the adaptation of pre-trained models for specific tasks.
no code implementations • 25 Mar 2024 • Sanyam Lakhanpal, Shivang Chopra, Vinija Jain, Aman Chadha, Man Luo
We introduce a benchmark, LenCom-Eval, specifically designed for testing models' capability in generating images with Lengthy and Complex visual text.
Optical Character Recognition (OCR) Text-to-Image Generation
no code implementations • 4 Mar 2024 • Fiona Anting Tan, Gerard Christopher Yeo, Fanyou Wu, Weijie Xu, Vinija Jain, Aman Chadha, Kokil Jaidka, Yang Liu, See-Kiong Ng
Drawing inspiration from psychological research on the links between certain personality traits and Theory-of-Mind (ToM) reasoning, and from prompt engineering research on the hyper-sensitivity of prompts in affecting LLMs capabilities, this study investigates how inducing personalities in LLMs using prompts affects their ToM reasoning capabilities.
no code implementations • 3 Mar 2024 • Arijit Ghosh Chowdhury, Md Mofijul Islam, Faysal Hossain Shezan, Vaibhav Kumar, Vinija Jain, Aman Chadha
Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text.
no code implementations • 28 Feb 2024 • Swagata Ashwani, Kshiteesh Hegde, Nishith Reddy Mannuru, Mayank Jindal, Dushyant Singh Sengar, Krishna Chaitanya Rao Kathala, Dishant Banga, Vinija Jain, Aman Chadha
The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning.
no code implementations • 20 Feb 2024 • Akash Ghosh, Arkadeep Acharya, Sriparna Saha, Vinija Jain, Aman Chadha
The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution.
no code implementations • 18 Feb 2024 • Aishik Rakshit, Smriti Singh, Shuvam Keshari, Arijit Ghosh Chowdhury, Vinija Jain, Aman Chadha
Embeddings play a pivotal role in the efficacy of Large Language Models.
no code implementations • 16 Feb 2024 • Smriti Singh, Shuvam Keshari, Vinija Jain, Aman Chadha
Socioeconomic bias in society exacerbates disparities, influencing access to opportunities and resources based on individuals' economic and social backgrounds.
no code implementations • 11 Feb 2024 • Arpita Vats, Vinija Jain, Rahul Raja, Aman Chadha
The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional recommenders.
no code implementations • 5 Feb 2024 • Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha
This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters.
no code implementations • 15 Jan 2024 • Saurav Pawar, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, Aman Chadha, Amitava Das
The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP), contributing to substantial progress in both text comprehension and generation.
1 code implementation • 2 Jan 2024 • S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, Amitava Das
As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded.
no code implementations • 12 Dec 2023 • Ibtihel Amara, Vinija Jain, Aman Chadha
We tackle the challenging issue of aggressive fine-tuning encountered during the process of transfer learning of pre-trained language models (PLMs) with limited labeled downstream data.
no code implementations • 1 Dec 2023 • Anku Rani, Dwip Dalal, Shreya Gautam, Pankaj Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
This research explores the problem of deception through the lens of psychology, employing a framework that categorizes deception into three forms: lies of omission, lies of commission, and lies of influence.
1 code implementation • 11 Oct 2023 • Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
Through our analysis, we find that LLMs' ability to identify sentence analogies is positively correlated with their ability to encode syntactic and semantic structures of sentences.
no code implementations • 8 Oct 2023 • Megha Chakraborty, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Krish Sharma, Niyar R Barman, Chandan Gupta, Shreya Gautam, Tanay Kumar, Vinija Jain, Aman Chadha, Amit P. Sheth, Amitava Das
Given this cynosural spotlight on generative AI, AI-generated text detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by emergence of techniques to bypass detection.
no code implementations • 11 Sep 2023 • Mohsin Ali, Kandukuri Sai Teja, Neeharika Gupta, Parth Patwa, Anubhab Chatterjee, Vinija Jain, Aman Chadha, Amitava Das
Therefore, to enrich word information and incorporate positional information, positional encoding is defined.
no code implementations • 19 Feb 2023 • Aman Chadha, Vinija Jain
While few-shot learning as a transfer learning paradigm has gained significant traction for scenarios with limited data, it has primarily been explored in the context of building unimodal and unilingual models.
no code implementations • 25 Jun 2021 • Aman Chadha, Vinija Jain
We demonstrate the effectiveness of iReason using a two-pronged comparative analysis with language representation learning models (BERT, GPT-2) as well as current state-of-the-art multimodal causality models.