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Notes
ReAct: Synergizing Reasoning and Acting in Language Models
Combines reasoning traces with environment actions so agents can plan, observe, and revise while solving tasks.
ReadingToolformer: Language Models Can Teach Themselves to Use Tools
Explores how language models can learn to call external tools through self-supervised data generation.
NotesRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Introduces RAG, a framework that combines parametric generation with non-parametric retrieval so answers can be grounded in external documents.
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