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📄 **[Read on arXiv](https://arxiv.org/abs/2201.12086)** Vision-language pre-training (VLP) methods before BLIP suffered from two fundamental limitations: (1) model architectures were typically optimized for either under…

📄 **[Read on arXiv](https://arxiv.org/abs/2501.12948)** DeepSeek-R1 demonstrates that sophisticated reasoning capabilities -- including self-verification, reflection, and extended chain-of-thought -- can emerge in large…

📄 **[Read on arXiv](https://arxiv.org/abs/2305.18290)** Aligning large language models (LLMs) with human preferences has traditionally required reinforcement learning from human feedback (RLHF), a complex multi-stage pi…

📄 **[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/2204.14198)** Flamingo, developed by DeepMind, is a family of visual language models that extend the in-context few-shot learning ability of large language models to multimodal…

📄 **[Read on arXiv](https://arxiv.org/abs/2507.06261)** Gemini 2.5 is Google's frontier multimodal model family, built on a sparse Mixture-of-Experts (MoE) Transformer architecture. It represents a major advance in reas…

📄 **[Read on arXiv](https://arxiv.org/abs/2303.08774)** GPT-4 is a large-scale multimodal Transformer model developed by OpenAI that accepts both image and text inputs and produces text outputs. It represents a major st…

📄 **[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/2103.00020)** CLIP (Contrastive Language-Image Pre-training) learns visual representations from natural language supervision by training an image encoder and a text encoder join…

📄 **[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/2106.09685)** As pretrained language models grow to hundreds of billions of parameters, full fine-tuning -- updating every weight for each downstream task -- becomes prohibitive…

📄 **[Read on arXiv](https://arxiv.org/abs/2312.00752)** Transformers have dominated sequence modeling since 2017, but their quadratic-complexity self-attention mechanism creates a fundamental bottleneck for long sequenc…

📄 **[Read on arXiv](https://arxiv.org/abs/2310.06825)** Mistral 7B (Jiang et al., Mistral AI, 2023) challenged the prevailing assumption that larger language models are always better by demonstrating that a carefully de…

📄 **[Read on arXiv](https://arxiv.org/abs/2401.04088)** Mixtral 8x7B, developed by Mistral AI, introduces a Sparse Mixture-of-Experts (SMoE) language model that achieves the quality of much larger dense models at a frac…

📄 **[Read on arXiv](https://arxiv.org/abs/2108.07258)** "On the Opportunities and Risks of Foundation Models" is a comprehensive 200+ page report from over 100 researchers at Stanford's Center for Research on Foundation…

📄 **[Read on arXiv](https://arxiv.org/abs/2101.00190)** Large pretrained language models like GPT-2 and BART achieve strong performance on generation tasks, but full fine-tuning requires storing a separate copy of all m…

📄 **[Read on arXiv](https://arxiv.org/abs/2305.14314)** Full fine-tuning of large language models requires enormous GPU memory -- a 65B-parameter model in 16-bit precision needs over 780 GB of GPU memory for parameters…

📄 **[Read on arXiv](https://arxiv.org/abs/2505.09388)** Qwen3, developed by the Qwen team at Alibaba, represents a major step forward in open-weight language models by offering a comprehensive family spanning both dense…

📄 **[Read on arXiv](https://arxiv.org/abs/2210.03629)** Large language models had demonstrated two powerful capabilities in isolation: chain-of-thought reasoning for multi-step problem solving, and action generation for…

📄 **[Read on arXiv](https://arxiv.org/abs/2210.11416)** Large language models exhibit strong few-shot capabilities, but their ability to follow instructions and generalize to unseen tasks remains limited without targete…

📄 **[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/2302.04761)** Large language models exhibit remarkable in-context learning abilities but paradoxically struggle with tasks that are trivial for simple external tools -- arithmet…

📄 **[Read on arXiv](https://arxiv.org/abs/2203.15556)** The Chinchilla paper (Hoffmann et al., DeepMind, 2022) is one of the most consequential papers in the LLM era because it corrected the field's scaling intuition. K…

📄 **[Read on arXiv](https://arxiv.org/abs/2203.02155)** Large language models like GPT-3 are trained on vast internet corpora to predict the next token, but this objective is fundamentally misaligned with the goal of fo…

📄 **[Read on arXiv](https://arxiv.org/abs/2305.10601)** Language models are typically used in a left-to-right token-generation mode, which limits their ability to explore alternative reasoning paths or backtrack from mi…

📄 **[Read on arXiv](https://arxiv.org/abs/2304.08485)** Large language models transformed NLP through instruction tuning -- training on diverse instruction-response pairs so models follow human intent across tasks. Visu…