6 papers
Unveiling Reasoning Thresholds in Language Models: Scaling, Fine-Tuning, and Interpretability through Attention Maps
Yen-Che Hsiao, Abhishek Dutta
This study investigates the in-context learning capabilities of various decoder-only transformer-based language models with different model sizes and training data, including GPT2,…
Towards Autonomous Agents: Adaptive-planning, Reasoning, and Acting in Language Models
Abhishek Dutta, Yen-Che Hsiao
We propose a novel in-context learning algorithm for building autonomous decision-making language agents. The language agent continuously attempts to solve the same task by self-co…
Adaptive Reasoning and Acting in Medical Language Agents
Abhishek Dutta, Yen-Che Hsiao
This paper presents an innovative large language model (LLM) agent framework for enhancing diagnostic accuracy in simulated clinical environments using the AgentClinic benchmark. T…
Efficient transformer with reinforced position embedding for language models
Yen-Che Hsiao, Abhishek Dutta
In this paper, we propose an efficient transformer architecture that uses reinforced positional embedding to obtain superior performance with half the number of encoder decoder lay…
Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial Intelligence
Yen-Che Hsiao, Rongting Yue, Abhishek Dutta
This paper provides a comprehensive and detailed derivation of the backpropagation algorithm for graph convolutional neural networks using matrix calculus. The derivation is extend…
Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent
Yen-Che Hsiao, Abhishek Dutta
This paper presents a novel coordinate descent algorithm leveraging a combination of one-directional line search and gradient information for parameter updates for a squared error…