10 citations · 35 across the 23 of their papers we have counts for
13 papers · 1 filter
Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification
Nathaniel Pinckney, Chenhui Deng, Chia-Tung Ho +5
We present the Comprehensive Verilog Design Problems (CVDP) benchmark, a new dataset and infrastructure to advance LLM and agent research in hardware design and verification. CVDP…
Generalizing Large Language Model Usability Across Resource-Constrained
Yun-Da Tsai
Large Language Models (LLMs) have achieved remarkable success across a wide range of natural language tasks, and recent efforts have sought to extend their capabilities to multimod…
Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection
Pei-Fu Guo, Yun-Da Tsai, Shou-De Lin
Large language models (LLMs) often generate fluent but factually incorrect outputs, known as hallucinations, which undermine their reliability in real-world applications. While unc…
Benchmarking Large Language Model Uncertainty for Prompt Optimization
Pei-Fu Guo, Yun-Da Tsai, Shou-De Lin
Prompt optimization algorithms for Large Language Models (LLMs) excel in multi-step reasoning but still lack effective uncertainty estimation. This paper introduces a benchmark dat…
Enhance Modality Robustness in Text-Centric Multimodal Alignment with Adversarial Prompting
Yun-Da Tsai, Ting-Yu Yen, Keng-Te Liao +1
Converting different modalities into generalized text, which then serves as input prompts for large language models (LLMs), is a common approach for aligning multimodal models, par…
LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding Linear Bandit Problem
Yun-Ang Wu, Yun-Da Tsai, Shou-De Lin
In this study, we delve into the Thresholding Linear Bandit (TLB) problem, a nuanced domain within stochastic Multi-Armed Bandit (MAB) problems, focusing on maximizing decision acc…
Text-centric Alignment for Multi-Modality Learning
Yun-Da Tsai, Ting-Yu Yen, Pei-Fu Guo +2
This research paper addresses the challenge of modality mismatch in multimodal learning, where the modalities available during inference differ from those available at training. We…
lil'HDoC: An Algorithm for Good Arm Identification under Small Threshold Gap
Tzu-Hsien Tsai, Yun-Da Tsai, Shou-De Lin
Good arm identification (GAI) is a pure-exploration bandit problem in which a single learner outputs an arm as soon as it is identified as a good arm. A good arm is defined as an a…
Towards Optimizing with Large Language Models
Pei-Fu Guo, Ying-Hsuan Chen, Yun-Da Tsai +1
In this work, we conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes. Each of these tasks corresponds to unique optimization domains,…
AutoML-GPT: Large Language Model for AutoML
Yun-Da Tsai, Yu-Che Tsai, Bo-Wei Huang +2
With the emerging trend of GPT models, we have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. This framework grants users acc…
Differential Good Arm Identification
Yun-Da Tsai, Tzu-Hsien Tsai, Shou-De Lin
This paper targets a variant of the stochastic multi-armed bandit problem called good arm identification (GAI). GAI is a pure-exploration bandit problem with the goal to output as…
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network
Yun Da Tsai, Shou De Lin
This work addresses the efficiency concern on inferring a nonlinear contextual bandit when the number of arms is very large. We propose a neural bandit model with an end-to-end…
Toward More Generalized Malicious URL Detection Models
YunDa Tsai, Cayon Liow, Yin Sheng Siang +1
This paper reveals a data bias issue that can severely affect the performance while conducting a machine learning model for malicious URL detection. We describe how such bias can b…