3 citations · 3 across the 4 of their papers we have counts for
5 papers
On Mitigation of Subliminal Learning in Large Language Models
Atsushi Yanagisawa, Brendan Gho, Rajendran Ramesh Babu Manoj Narender +3
Knowledge distillation can transmit unintended behavioral traits from a teacher model to a student through training data that appear semantically unrelated to those traits, a pheno…
Talked Out of the Truth: Sycophancy in the Reasoning Chains of Multimodal Models
Mahir Numayeer Islam, Gakuto Okuyama, Nikolaus Siauw +3
Large multimodal reasoning models (LMRMs) are increasingly capable, largely through generating explicit chain-of-thought reasoning before answering, but in language models this oft…
InversionView: A General-Purpose Method for Reading Information from Neural Activations
Xinting Huang, Madhur Panwar, Navin Goyal +1
The inner workings of neural networks can be better understood if we can fully decipher the information encoded in neural activations. In this paper, we argue that this information…
Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers
Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar +4
Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly en…
TAN-NTM: Topic Attention Networks for Neural Topic Modeling
Madhur Panwar, Shashank Shailabh, Milan Aggarwal +1
Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take documen…