Correcting Mode Collapse in Silicon Sampling with Semantic Similarity Rating

Silicon sampling refers to the use of Large Language Models (LLMs) to generate responses to surveys. It has shown promise, but tends to generate response distributions with unrealistically low variance. We argue that this mode collapse is due to LLMs failure to generate numeric data, and that text responses may be better suited for this task. We analyze whether Semantic Similarity Rating can im…