EnvSimBench reveals that state-of-the-art LLMs exhibit a universal state change cliff in environment simulation, with a new constraint-driven pipeline raising synthesis yield by 6.8% and cutting costs over 90%.
Measuring and improving consistency in pretrained language models.Transactions of the Association for Computational Linguistics, 9:1012–1031
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EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation
EnvSimBench reveals that state-of-the-art LLMs exhibit a universal state change cliff in environment simulation, with a new constraint-driven pipeline raising synthesis yield by 6.8% and cutting costs over 90%.