Papers

📄 **[Read on arXiv](https://arxiv.org/abs/2312.09397)** Talk2Drive introduces an LLM-based framework for personalized autonomous driving through natural language interaction, demonstrated in real-world field experiments…

📄 **[Read on arXiv](https://arxiv.org/abs/2312.07488)** LMDrive is the first system to demonstrate and benchmark LLM-based driving in closed-loop simulation, introducing the LangAuto benchmark with ~64K instruction-foll…

📄 **[Read on arXiv](https://arxiv.org/abs/2402.01817)** This paper by Subbarao Kambhampati and colleagues at Arizona State University addresses one of the most important questions in modern AI: can large language models…

📄 **[Read on arXiv](https://arxiv.org/abs/2311.10813)** Agent-Driver reframes autonomous driving as a cognitive agent problem, positioning a large language model as the central reasoning and planning engine rather than…

📄 **[Read on arXiv](https://arxiv.org/abs/2307.09288)** Llama 2 (Touvron et al., Meta AI, 2023) addresses the gap between open-source pretrained language models and polished, closed-source "product" LLMs like ChatGPT. W…

📄 **[Read on arXiv](https://arxiv.org/abs/2310.03026)** LanguageMPC addresses a fundamental limitation in autonomous driving: traditional planners (MPC, RL) struggle with complex scenarios that require high-level reason…

📄 **[Read on arXiv](https://arxiv.org/abs/2310.01415)** GPT-Driver reformulates autonomous driving motion planning as a language modeling problem. Scene context (object positions, velocities, lane geometry) and ego vehi…

📄 **[Read on arXiv](https://arxiv.org/abs/2312.09245)** DriveMLM proposes using a multimodal LLM as a plug-and-play behavioral planning module within existing autonomous driving stacks (Apollo, Autoware), rather than re…

📄 **[Read on arXiv](https://arxiv.org/abs/2309.10228)** Drive as You Speak (DAYS) proposes a framework for enabling natural language interaction between human passengers and autonomous vehicles using large language mode…

📄 **[Read on arXiv](https://arxiv.org/abs/2201.11903)** Wei et al., arXiv 2201.11903, 2022 (NeurIPS 2022). - [Paper](https://arxiv.org/abs/2201.11903) Chain-of-thought (CoT) prompting demonstrates that including interme…

📄 **[Read on arXiv](https://arxiv.org/abs/2001.08361)** This is the canonical early scaling-law paper for language models, authored by Kaplan et al. at OpenAI. It demonstrated that neural language model cross-entropy lo…

📄 **[Read on arXiv](https://arxiv.org/abs/2005.14165)** GPT-3 is a 175 billion parameter autoregressive language model that demonstrated a remarkable emergent capability: in-context learning, where the model performs ne…

📄 **[Read on arXiv](https://arxiv.org/abs/1810.04805)** Devlin, Chang, Lee, Toutanova (Google AI Language), NAACL, 2019. - [Paper](https://aclanthology.org/N19-1423/) - [arXiv](https://arxiv.org/abs/1810.04805) BERT (Bi…

📄 **[Read on arXiv](https://arxiv.org/abs/1706.03762)** Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin, NeurIPS, 2017. - [Paper](https://arxiv.org/abs/1706.03762) - [The Annotated Transformer](htt…