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--- title: "Language Agents with Karthik" type: concept source: https://www.jemoka.com/posts/kbhlanguage_agents/ confidence: high status: active --- Transitions Transition first from rule based learning to statistical learning Rise of semantic parsing: statistical models of parsing Then, moving from semantic parsing to large models—putting decision making and language modeling into the same bubble Importance of LLMs They are simply better at understanding language inputs They can generate structured information (i.e. not just human language, JSONs, etc.) They can perform natural language “reasoning”—not just generate (and natural language generation, abv) 1+3 gives you chain of thought reasoning 1+2 gives CALM, SayCan, and other types of RL text parsing in order to do stuff with robotics all three gives ReAct ReAct See ReAct Problem: agents are not robust at all https://github.com/ryoungj/ToolEmu Key Challenges See History of Agents and Their Challenges