4 papers
Assessing Large Language Models in Generating RTL Design Specifications
Hung-Ming Huang, Yu-Hsin Yang, Fu-Chieh Chang +5
As IC design grows more complex, automating comprehension and documentation of RTL code has become increasingly important. Engineers currently should manually interpret existing RT…
Unveiling the Latent Directions of Reflection in Large Language Models
Fu-Chieh Chang, Yu-Ting Lee, Pei-Yuan Wu
Reflection, the ability of large language models (LLMs) to evaluate and revise their own reasoning, has been widely used to improve performance on complex reasoning tasks. Yet, mos…
A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes
Yu Wang, Fu-Chieh Chang, Pei-Yuan Wu
We study the robustness of Transformer language models under semantic out-of-distribution (OOD) shifts, where training and test data lie in disjoint latent spaces. Using Wasserstei…
Unraveling Arithmetic in Large Language Models: The Role of Algebraic Structures
Fu-Chieh Chang, You-Chen Lin, Pei-Yuan Wu
Large language models (LLMs) have demonstrated remarkable mathematical capabilities, largely driven by chain-of-thought (CoT) prompting, which decomposes complex reasoning into ste…