[PDF] arxiv.org

A comprehensive overview of large language models

H Naveed, AU Khan, S Qiu, M Saqib, S Anwar… - arXiv preprint arXiv …, 2023 - arxiv.org
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …
被引用次数：211 相关文章 所有 3 个版本
[PDF] arxiv.org

DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiences

Y Huang, J Sansom, Z Ma, F Gervits, J Chai - arXiv preprint arXiv …, 2024 - arxiv.org
Recent advancements in foundation models (FMs) have unlocked new prospects in
autonomous driving, yet the experimental settings of these studies are preliminary, over …
被引用次数：1 相关文章 所有 3 个版本
[PDF] arxiv.org

Dilu: A knowledge-driven approach to autonomous driving with large language models

L Wen, D Fu, X Li, X Cai, T Ma, P Cai, M Dou… - arXiv preprint arXiv …, 2023 - arxiv.org
Recent advancements in autonomous driving have relied on data-driven approaches, which
are widely adopted but face challenges including dataset bias, overfitting, and …
被引用次数：55 相关文章 所有 4 个版本
[PDF] arxiv.org

Dolphins: Multimodal language model for driving

Y Ma, Y Cao, J Sun, M Pavone, C Xiao - arXiv preprint arXiv:2312.00438, 2023 - arxiv.org
The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world
scenarios with human-like understanding and responsiveness. In this paper, we introduce …
被引用次数：18 相关文章 所有 2 个版本
[PDF] arxiv.org

Corex: Pushing the boundaries of complex reasoning through multi-model collaboration

Q Sun, Z Yin, X Li, Z Wu, X Qiu, L Kong - arXiv preprint arXiv:2310.00280, 2023 - arxiv.org
Large Language Models (LLMs) are evolving at an unprecedented pace and have exhibited
considerable capability in the realm of natural language processing (NLP) with world …
被引用次数：6 相关文章 所有 4 个版本
[PDF] arxiv.org

Cumo: Scaling multimodal llm with co-upcycled mixture-of-experts

J Li, X Wang, S Zhu, CW Kuo, L Xu, F Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent advancements in Multimodal Large Language Models (LLMs) have focused
primarily on scaling by increasing text-image pair data and enhancing LLMs to improve …
被引用次数：4 相关文章 所有 2 个版本
[PDF] arxiv.org

Anygpt: Unified multimodal llm with discrete sequence modeling

J Zhan, J Dai, J Ye, Y Zhou, D Zhang, Z Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete
representations for the unified processing of various modalities, including speech, text …
被引用次数：18 相关文章 所有 2 个版本

Anymal: An efficient and scalable any-modality augmented language model

S Moon, A Madotto, Z Lin, T Nagarajan, M Smith… - arXiv preprint arXiv …, 2023 - arxiv.org
We present Any-Modality Augmented Language Model (AnyMAL), a unified model that
reasons over diverse input modality signals (ie text, image, video, audio, IMU motion …
被引用次数：39 相关文章 所有 2 个版本
[PDF] arxiv.org

Surrealdriver: Designing generative driver agent simulation framework in urban contexts based on large language model

Y Jin, X Shen, H Peng, X Liu, J Qin, J Li, J Xie… - arXiv preprint arXiv …, 2023 - arxiv.org
Simulation plays a critical role in the research and development of autonomous driving and
intelligent transportation systems. However, the current simulation platforms exhibit …
被引用次数：24 相关文章 所有 2 个版本
[PDF] arxiv.org

Mixture-of-loras: An efficient multitask tuning for large language models

W Feng, C Hao, Y Zhang, Y Han, H Wang - arXiv preprint arXiv …, 2024 - arxiv.org
Instruction Tuning has the potential to stimulate or enhance specific capabilities of large
language models (LLMs). However, achieving the right balance of data is crucial to prevent …
被引用次数：10 相关文章 所有 2 个版本