10 citations · 35 across the 23 of their papers we have counts for
23 papers
Beyond Facts: Benchmarking Distributional Reading Comprehension in Large Language Models
Pei-Fu Guo, Ya-An Tsai, Chun-Chia Hsu +6
While most reading comprehension benchmarks for LLMs focus on factual information that can be answered by localizing specific textual evidence, many real-world tasks require unders…
An Efficient Algorithm for Thresholding Monte Carlo Tree Search
Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1
We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\mathc…
LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs
Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu +6
Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior ex…
Multimodal Chip Physical Design Engineer Assistant
Yun-Da Tsai, Chang-Yu Chao, Liang-Yeh Shen +7
Modern chip physical design relies heavily on Electronic Design Automation (EDA) tools, which often struggle to provide interpretable feedback or actionable guidance for improving…
ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Chenhui Deng, Yun-Da Tsai, Guan-Ting Liu +2
Recent advances in large language models (LLMs) have enabled near-human performance on software coding benchmarks, but their effectiveness in RTL code generation remains limited du…
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…
CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair
Mingjie Liu, Yun-Da Tsai, Wenfei Zhou +1
Despite the significant progress made in code generation with large language models, challenges persist, especially with hardware description languages such as Verilog. This paper…
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…
Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning
Yun-Da Tsai, Mingjie Liu, Haoxing Ren
Recent work targeting large language models (LLMs) for code generation demonstrated that increasing the amount of training data through synthetic code generation often leads to exc…
Enhance the Robustness of Text-Centric Multimodal Alignments
Ting-Yu Yen, Yun-Da Tsai, Keng-Te Liao +1
Converting different modalities into general text, serving as input prompts for large language models (LLMs), is a common method to align multimodal models when there is limited pa…
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…
RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models
Yun-Da Tsai, Mingjie Liu, Haoxing Ren
This paper presents RTLFixer, a novel framework enabling automatic syntax errors fixing for Verilog code with Large Language Models (LLMs). Despite LLM's promising capabilities, ou…
PSGText: Stroke-Guided Scene Text Editing with PSP Module
Felix Liawi, Yun-Da Tsai, Guan-Lun Lu +1
Scene Text Editing (STE) aims to substitute text in an image with new desired text while preserving the background and styles of the original text. However, present techniques pres…
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…