9 citations · 11 across the 11 of their papers we have counts for
6 papers · 1 filter
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…