VS-CoT

A VS variant that adds chain-of-thought reasoning before generating the distribution of responses with probabilities

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VS-CoT and VS-Multi push the Pareto front of the diversity-quality trade-off across creative writing tasksclaim0.779
Empirical finding from creative writing experiments showing VS variants achieve higher diversity without sacrificing quality
VS-Standardmethod0.750
The baseline variant of Verbalized Sampling that asks for k responses with their probabilities in a single LLM call
CoT Promptingmethod0.734
Chain-of-thought prompting baseline used for comparison in creative writing and other tasks
VS-Multimethod0.721
A VS variant that generates k responses with probabilities across multiple conversation turns for additional diversity
CoT Monitormethod0.718
Named method for monitoring chain-of-thought text to detect when the model signals its answer, compared against activation probes
Multimodal-CoTframework0.717
A two-stage framework that separates rationale generation and answer inference by incorporating vision and language modalities.
Chain-of-Thought (CoT)framework0.715
A prompting technique that elicits intermediate reasoning steps before final answer inference in language models.
VS-CoT achieves the highest Top@1 accuracy (0.348) and Pass@N accuracy (0.485) on SimpleQA among all methods testedfinding0.703
Shows VS not only maintains but can slightly improve factual accuracy compared to baseline methods