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language, end to end
learn nlp
embeddings, the transformer, pretrained models, and building real apps on top.
the curated path
curatedmixed~4 weeks, part-time
nlp & the transformer
the modern nlp stack from the ground up: represent text, understand the transformer deeply, then fine-tune and ship.
4 modules · 13 resources · checkpoint per modulestay current
what's new in nlp
- Can small language models handle context-summarized multi-turn customer-service QA? A synthetic data-driven comparative evaluationevaluates whether smaller language models can handle multi-turn customer service conversations when context is summarized, using synthetic data for benchmarking. this is practical for teams wanting to deploy qa systems at lower cost and latency without relying on large models.
- Superintelligent Retrieval Agent: The Next Frontier of Information Retrievalintroduces an agent-based approach to retrieval that moves beyond treating retrieval as a black box, enabling more intelligent search over large knowledge bases. practitioners managing rag systems and knowledge bases will find this relevant for improving how agents decide what to retrieve and when to refine queries.
- Distilling structural reasoning: efficient semantic parsing via chain-of-thought rationalization and contrastive demonstration selectionshows how to distill chain-of-thought reasoning into smaller semantic parsing models by selecting contrastive demonstrations, reducing model size while maintaining accuracy on complex compositional queries. this helps teams deploy semantic parsing in resource-constrained environments without sacrificing reasoning quality.
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