5 citations · 13 across the 9 of their papers we have counts for
4 papers · 1 filter
Understanding and Steering the Cognitive Behaviors of Reasoning Models at Test-Time
Zhenyu Zhang, Xiaoxia Wu, Zhongzhu Zhou +7
Large Language Models (LLMs) often rely on long chain-of-thought (CoT) reasoning to solve complex tasks. While effective, these trajectories are frequently inefficient, leading to…
Training-Free Activation Sparsity in Large Language Models
James Liu, Pragaash Ponnusamy, Tianle Cai +3
Activation sparsity can enable practical inference speedups in large language models (LLMs) by reducing the compute and memory-movement required for matrix multiplications during t…
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI
Pragaash Ponnusamy, Clint Solomon Mathialagan, Gustavo Aguilar +2
Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean. However, such learning, particu…
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning
Md Mofijul Islam, Gustavo Aguilar, Pragaash Ponnusamy +3
Subword tokenization is a commonly used input pre-processing step in most recent NLP models. However, it limits the models' ability to leverage end-to-end task learning. Its freque…