U-Define improves user control in LLM planning by letting people define hard rules and soft preferences in natural language with matching verification methods, raising usefulness and satisfaction scores.
we need structured output
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 2representative citing papers
FSTS automates multi-agent social experiment design via LLM script generation across three phases, with tests indicating reproduction of real-world outcomes.
citing papers explorer
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U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning
U-Define improves user control in LLM planning by letting people define hard rules and soft preferences in natural language with matching verification methods, raising usefulness and satisfaction scores.
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From Script to Stage: Automating Experimental Design for Social Simulations with LLMs
FSTS automates multi-agent social experiment design via LLM script generation across three phases, with tests indicating reproduction of real-world outcomes.