9 citations · 14 across the 17 of their papers we have counts for
8 papers · 1 filter
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…