Strategic Self-Consistency

The paper "Strategic Self-Consistency" investigates how large language model providers might exploit the self‑consistency technique—generating multiple reasoning paths and selecting the majority answer—to overcharge users. The authors present a simple, efficient algorithm that strategically generates and reorders extra reasoning paths so that each appears necessary for the majority vote, thereby evading detection by auditors. Experiments on Llama, Qwen, and DeepSeek-R1 models across math, science, and QA benchmarks show that the added paths follow a heavy‑tailed distribution and that significant overcharging can persist even under stringent audits with a false‑positive rate below 0.1.

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• llms
• nlp
• benchmarks