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Bahdanau attention replaced fixed context vectors with dynamic source alignment, improving neural machine translation and shaping later transformer models.
Covers hybrid retrieval systems introduced in 2024. Explains how hybrid systems combine sparse retrieval for fast candidate generation.
Covers structured outputs introduced in language models during 2024. Explains how structured outputs enable reliable data extraction.
Covers multimodal integration in 2024, the advance that enabled AI systems to directly process and understand text, images, audio.
Covers advanced parameter-efficient fine-tuning methods introduced in 2024, including AdaLoRA, DoRA, VeRA, and other innovations.
Covers continuous post-training, including parameter-efficient fine-tuning with LoRA, catastrophic forgetting prevention, incremental model updates.
Covers GPT-4o, including unified multimodal architecture, real-time processing, unified tokenization, advanced attention mechanisms, memory mechanisms.
DeepSeek R1 uses reinforcement learning and distilled reasoning models for mathematical and coding tasks. Covers its training design, results, and limitations.
Covers agentic AI systems introduced in 2024. Explains how AI systems evolved from reactive tools to autonomous agents capable of planning.
AI co-scientist systems generate hypotheses, plan experiments, and synthesize evidence. Covers autonomous research workflows, evaluation, and human oversight.
Covers V-JEPA 2, including vision-based world modeling, joint embedding predictive architecture, visual prediction, embodied AI.
Examines Mistral AI's Mixtral models and how they demonstrated that sparse mixture-of-experts architectures could be production-ready.
Covers specialized large language models for low-resource languages, including synthetic data generation, cross-lingual transfer learning.
Covers Constitutional AI, including principle-based alignment, self-critique training, reinforcement learning from AI feedback (RLAIF), scalability advantages.
Examines multimodal large language models that integrated vision and language capabilities, enabling AI systems to process images and text together.
Covers the 2023 open LLM wave, including MPT, Falcon, Mistral, and other open models. Explains how these models created a competitive ecosystem.
LLaMA showed that carefully trained smaller models could match larger systems. Covers model sizes, data choices, efficiency, and open research access.
Covers GPT-4, including multimodal capabilities, improved reasoning abilities, enhanced safety and alignment, human-level performance on standardized tests.
Covers BIG-bench (Beyond the Imitation Game Benchmark) and MMLU (Massive Multitask Language Understanding), the widely used evaluation benchmarks.
Covers function calling capabilities in language models from 2023, including structured outputs, tool interaction, API integration.
QLoRA combines 4-bit quantization with low-rank adapters to fine-tune large language models on limited hardware while preserving model quality.
Whisper applies encoder-decoder transformers to multilingual speech recognition, translation, and language identification across noisy audio conditions.
Flamingo connects frozen vision and language models through gated cross-attention, supporting few-shot image and video tasks without task-specific fine-tuning.
Covers Google's PaLM, the 540 billion parameter language model that demonstrated advance capabilities in complex reasoning, multilingual understanding.
Covers HELM (Holistic Evaluation of Language Models), the early evaluation framework.
Covers multi-vector retrieval systems introduced in 2021. Explains how token-level contextualized embeddings enabled fine-grained matching.
Chain-of-thought prompting elicits intermediate reasoning steps from language models. Covers zero-shot and few-shot methods, benefits, and limits.
Covers the 2021 Foundation Models Report published by Stanford's CRFM. Explains how this influential report formally defined foundation models.
Covers Mixture of Experts (MoE) architectures, including routing mechanisms, load balancing, emergent specialization.
Covers OpenAI's InstructGPT research from 2022, including the three-stage RLHF training process, supervised fine-tuning, reward modeling.
Covers EleutherAI's The Pile, the early 825GB open-source dataset that broadened access to high-quality training data for large language models.
Covers Dense Passage Retrieval (DPR) and Retrieval-Augmented Generation (RAG), the 2020 innovations.
Covers BLOOM, the BigScience collaboration's 176-billion-parameter open-access multilingual language model released in 2022.
Covers the 2020 scaling laws discovered by Kaplan et al. Explains how power-law relationships predict model performance from scale.
Covers the Chinchilla scaling laws introduced in 2022. Explains how compute-optimal training balances model size and training data.
Stable Diffusion generates images in a compressed latent space. Covers text conditioning, the denoising process, training, inference, and model accessibility.
Covers FlashAttention introduced in 2022. Explains how IO-aware attention computation enabled 2-4x speedup and 5-10x memory reduction.
Covers OpenAI's CLIP, the early vision-language model that enables zero-shot image classification through contrastive learning.
Covers instruction tuning introduced in 2021. Explains how fine-tuning on diverse instruction-response pairs changed language models.
Examines how Mixture of Experts (MoE) architectures changed large language model scaling in 2024.
Covers OpenAI's DALL·E 2, the major text-to-image generation model that combined CLIP-guided diffusion with high-quality image synthesis.
Codex adapted GPT-3 for code completion and generation. Covers its training data, natural-language prompts, benchmark results, and role in developer tools.
Covers OpenAI's DALL·E, the early text-to-image generation model that extended transformer architectures to multimodal tasks.
GPT-3 showed that scaling autoregressive transformers could enable in-context and few-shot learning. Covers its architecture, data, prompting, and limitations.
Covers Google's T5 (Text-to-Text Transfer Transformer) introduced in 2019. Explains how the text-to-text framework unified diverse NLP tasks.
Covers GLUE and SuperGLUE benchmarks introduced in 2018. Explains how these standardized evaluation frameworks changed language AI research.
Transformer-XL extends context with segment recurrence and relative positions. Covers its architecture, efficiency, training method, and influence.
Covers BERT's application to information retrieval in 2019. Explains how transformer architectures changed search and ranking systems.
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