4 citations · 13 across the 10 of their papers we have counts for
12 papers
From Sparse Features to Trustworthy Proxies: Certifying SAE-Based Interpretability
Dibyanayan Bandyopadhyay, Asif Ekbal
Sparse autoencoders (SAEs) are increasingly used to extract interpretable features from language models (LMs), yet a central question remains: when can an SAE-based explanation be…
Sparse Semantic Dimension as a Generalization Certificate for LLMs
Dibyanayan Bandyopadhyay, Asif Ekbal
Standard statistical learning theory predicts that Large Language Models (LLMs) should overfit because their parameter counts vastly exceed the number of training tokens. Yet, in p…
CAuSE: Decoding Multimodal Classifiers using Faithful Natural Language Explanation
Dibyanayan Bandyopadhyay, Soham Bhattacharjee, Mohammed Hasanuzzaman +1
Multimodal classifiers function as opaque black box models. While several techniques exist to interpret their predictions, very few of them are as intuitive and accessible as natur…
Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
Baban Gain, Dibyanayan Bandyopadhyay, Asif Ekbal +1
Large Language Models (LLMs) are rapidly reshaping machine translation (MT), particularly by introducing instruction-following, in-context learning, and preference-based alignment…
Thinking Machines: A Survey of LLM based Reasoning Strategies
Dibyanayan Bandyopadhyay, Soham Bhattacharjee, Asif Ekbal
Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General…
Seeing Through VisualBERT: A Causal Adventure on Memetic Landscapes
Dibyanayan Bandyopadhyay, Mohammed Hasanuzzaman, Asif Ekbal
Detecting offensive memes is crucial, yet standard deep neural network systems often remain opaque. Various input attribution-based methods attempt to interpret their behavior, but…