Zobrazeno 1 - 10
of 10 274
pro vyhledávání: '"An, Yingbo"'
Recently, there has been growing interest in the capability of multimodal large language models (MLLMs) to process high-resolution images. A common approach currently involves dynamically cropping the original high-resolution image into smaller sub-i
Externí odkaz:
http://arxiv.org/abs/2412.08378
This paper studies the trajectory tracking and motion control problems for autonomous vehicles (AVs). A parameter adaptive control framework for AVs is proposed to enhance tracking accuracy and yaw stability. While establishing linear quadratic regul
Externí odkaz:
http://arxiv.org/abs/2411.17745
Large language models (LLMs) have demonstrated strong capabilities in text understanding and generation. However, they often lack factuality, producing a mixture of true and false information, especially in long-form generation. In this work, we inve
Externí odkaz:
http://arxiv.org/abs/2411.15993
Autor:
Liu, Ye, Meng, Rui, Joty, Shafiq, Savarese, Silvio, Xiong, Caiming, Zhou, Yingbo, Yavuz, Semih
Despite the success of text retrieval in many NLP tasks, code retrieval remains a largely underexplored area. Most text retrieval systems are tailored for natural language queries, often neglecting the specific challenges of retrieving code. This gap
Externí odkaz:
http://arxiv.org/abs/2411.12644
Pre-trained on massive amounts of code and text data, large language models (LLMs) have demonstrated remarkable achievements in performing code generation tasks. With additional execution-based feedback, these models can act as agents with capabiliti
Externí odkaz:
http://arxiv.org/abs/2411.04329
Accurate document retrieval is crucial for the success of retrieval-augmented generation (RAG) applications, including open-domain question answering and code completion. While large language models (LLMs) have been employed as dense encoders or list
Externí odkaz:
http://arxiv.org/abs/2411.00142
This paper studies the tracking problem of target with the partially unknown motion model by an active agent with bearing-only measurements using Gaussian process learning. To address this problem, a learning-planning-control framework is proposed. F
Externí odkaz:
http://arxiv.org/abs/2410.18669
This paper explores the rapid development of a telephone call summarization system utilizing large language models (LLMs). Our approach involves initial experiments with prompting existing LLMs to generate summaries of telephone conversations, follow
Externí odkaz:
http://arxiv.org/abs/2410.18624
Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attri
Externí odkaz:
http://arxiv.org/abs/2410.11273
Autor:
Han, Simeng, Yu, Aaron, Shen, Rui, Qi, Zhenting, Riddell, Martin, Zhou, Wenfei, Qiao, Yujie, Zhao, Yilun, Yavuz, Semih, Liu, Ye, Joty, Shafiq, Zhou, Yingbo, Xiong, Caiming, Radev, Dragomir, Ying, Rex, Cohan, Arman
Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales, which are not sufficient for proper investigation of model's capabilities. We present P-FOLI
Externí odkaz:
http://arxiv.org/abs/2410.09207