SPINACH does not guess complete SPARQL in one shot. It searches entities and properties, inspects Wikidata entries and examples, executes small queries, and composes a final query under explicit action and stopping rules.
CHURRO represents full-page text, layout, and metadata in HDML, unifies multilingual historical data for a page-level VLM, and connects extraction to HistoryGenie for searchable, conversational archives.
The final lecture is not a complete LLM-training tutorial. It studies data efficiency under fixed data and abundant compute, revisiting epochs, batches, ensembles, self-training, and conditions for synthetic continued pretraining.
The [WikiChat paper](https://aclanthology.org/2023.findings-emnlp.157/) expands RAG into query formulation, retrieval, filtering, generation, claim extraction, renewed retrieval and verification, and removal of unsupported content—and evaluates retrieval separately from factuality.
Fall 2025 opens with computational thinking: reliability comes from decomposing retrieval, formal representation, verification, and generation into testable algorithms, not from one heroic prompt.
STORM uses perspective-guided questions, simulated interviews, and outlines to broaden research; Co-STORM keeps a person in the loop so discovering unknown questions and co-editing become part of the system.
SLIDERS induces a question-specific schema, applies semantic chunking and contextualized extraction, reconciles duplicate rows, and answers with SUQL instead of feeding every long document directly to one model.
ReactGenie annotates React components to expose data, actions, and views, parses composite voice commands into a DSL, and renders native graphical output against shared UI context.
The lecture parses patient records and trial criteria into SMT, retrieves candidates through a weaker propositional projection, and runs a solver on the reduced set. Reasoning is inspectable, but NL-to-SMT remains the main error boundary.
Automated qualitative coding defines event types and arguments in a codebook, then separates document classification, structured extraction, and entity linking. Constrained JSON fixes form, not expert judgment.
Reliable database agents map language to executable queries, resolve schemas and enumerated values, and evaluate execution separately from answer generation; hybrid questions additionally require explicit source routing.
SUQL adds answer and summary functions over text to SQL. A semantic parser emits one hybrid query, while an optimizing compiler applies predicate pushdown, top-k pruning, and lazy evaluation.
CS224V splits task-agent evaluation into state updates and complete interaction: isolate the semantic parser, then test task completion, grounded queries, and valid actions with real users.
Genie Worksheets declare task capability as a form-like specification. A contextual semantic parser updates formal dialogue state while the runtime controls queries, actions, and responses.
Lecture 3 does not turn its survey of modern LLMs into a single best recipe. It finds a conservative consensus—pre-norm, RMSNorm, no biases, SwiGLU, and RoPE—plus a small set of deviations justified by inference cost or stability.
Lecture 4 studies two kinds of sparsity: linear/recurrent attention reduces sequence-length cost, while MoE activates only part of a model for each token. Both turn saved FLOPs into routing, balancing, communication, and kernel problems.
Lecture 14 moves raw documents through language, quality, and safety filtering; exact and near deduplication; and source mixing. Each stage reshapes model behavior, while synthetic instruction and agent trajectories extend the pipeline into executable environments.
Lecture 13 traces training sources through Common Crawl, Wikipedia, GitHub, arXiv, books, and open datasets. Technically accessible is not the same as licensed, and raw data is not training data; provenance must precede cleaning and mixing.
Lecture 12 moves from perplexity to exams, chat preferences, agents, reasoning, and safety. Every benchmark changes the capability definition, scaffold, judge, and contamination risk, so evaluation must first say whether it compares a method, model, or complete system.
Lecture 5 explains GPUs through SMs, warps, and the memory hierarchy, then unifies common optimization under low precision, fusion, recomputation, coalescing, and tiling. FlashAttention combines those principles for attention.