Zobrazeno 1 - 10
of 978
pro vyhledávání: '"Lin, Shou"'
Prompt optimization algorithms for Large Language Models (LLMs) excel in multi-step reasoning but still lack effective uncertainty estimation. This paper introduces a benchmark dataset to evaluate uncertainty metrics, focusing on Answer, Correctness,
Externí odkaz:
http://arxiv.org/abs/2409.10044
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, particularly when pairwise data is limited. Text-centric alignment method
Externí odkaz:
http://arxiv.org/abs/2408.09798
Autor:
Zhu, Kerui, Huang, Bo-Wei, Jin, Bowen, Jiao, Yizhu, Zhong, Ming, Chang, Kevin, Lin, Shou-De, Han, Jiawei
Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks, there's growing interest in applying LLMs to graph-related tasks. This study delves into the capabilities of instruction-following LLMs for engaging with real-world gra
Externí odkaz:
http://arxiv.org/abs/2408.05457
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 pairwise data. This text-centric approach leverages the unique propertie
Externí odkaz:
http://arxiv.org/abs/2407.05036
This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous research requiring direct model access, we explore a more realistic t
Externí odkaz:
http://arxiv.org/abs/2406.10280
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 accuracy against a linearly defined threshold under resource constraints.
Externí odkaz:
http://arxiv.org/abs/2403.06230
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 propose the Text-centric Alignment for Multi-Modality Learning (TAMML
Externí odkaz:
http://arxiv.org/abs/2402.08086
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 arm with an expected reward greater than or equal to a given threshold.
Externí odkaz:
http://arxiv.org/abs/2401.15879
Publikováno v:
In 2017 IEEE DSAA, pp. 496-503. IEEE, 2017
Recommender systems have been studied for decades with numerous promising models been proposed. Among them, Collaborative Filtering (CF) models are arguably the most successful one due to its high accuracy in recommendation and elimination of privacy
Externí odkaz:
http://arxiv.org/abs/2311.00612
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 present a notable challenge in the generation of edited text images that e
Externí odkaz:
http://arxiv.org/abs/2310.13366