9 citations · 11 across the 11 of their papers we have counts for
11 papers
Cyclic Counterfactuals under Shift-Scale Interventions
Saptarshi Saha, Dhruv Vansraj Rathore, Utpal Garain
Most counterfactual inference frameworks traditionally assume acyclic structural causal models (SCMs), i.e. directed acyclic graphs (DAGs). However, many real-world systems (e.g. b…
KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning?
Soumadeep Saha, Akshay Chaturvedi, Saptarshi Saha +2
Chain-of-thought (CoT) traces have been shown to improve performance of large language models on a plethora of reasoning tasks, yet there is no consensus on the mechanism by which…
On Measuring Intrinsic Causal Attributions in Deep Neural Networks
Saptarshi Saha, Dhruv Vansraj Rathore, Soumadeep Saha +2
Quantifying the causal influence of input features within neural networks has become a topic of increasing interest. Existing approaches typically assess direct, indirect, and tota…
sudoLLM: On Multi-role Alignment of Language Models
Soumadeep Saha, Akshay Chaturvedi, Joy Mahapatra +1
User authorization-based access privileges are a key feature in many safety-critical systems, but have not been extensively studied in the large language model (LLM) realm. In this…
Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment
A. Bonaldi, P. Hartley, R. Braun +179
We present and analyse the results of the Science data challenge 3a (SDC3a, https://sdc3.skao.int/challenges/foregrounds), an EoR foreground-removal community-wide exercise organis…
Factual Inconsistency in Data-to-Text Generation Scales Exponentially with LLM Size: A Statistical Validation
Joy Mahapatra, Soumyajit Roy, Utpal Garain
Monitoring factual inconsistency is essential for ensuring trustworthiness in data-to-text generation (D2T). While large language models (LLMs) have demonstrated exceptional perfor…
Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions
Rahul Shah, Purba Mukherjee, Soumadeep Saha +2
Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch from early universe observations by assuming a cos…
An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generation
Joy Mahapatra, Utpal Garain
Large Language Models (LLMs) have shown exceptional performance across various Data-to-Text Generation (DTG) tasks. However, generating factually consistent text in DTG remains cha…
Region Mixup
Saptarshi Saha, Utpal Garain
This paper introduces a simple extension of mixup (Zhang et al., 2018) data augmentation to enhance generalization in visual recognition tasks. Unlike the vanilla mixup method, whi…
Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation
Joy Mahapatra, Utpal Garain
Data-to-text (D2T) generation aims to generate human-readable text from semi-structured data, such as tables and graphs. The recent success of D2T is largely attributed to advancem…
Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification
Aditya Shankar Pal, Debojyoti Biswas, Joy Mahapatra +3
White blood cells (WBCs) play a crucial role in safeguarding the human body against pathogens and foreign substances. Leveraging the abundance of WBC imaging data and the power of…