Majority voting makes your AI dumber
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There's a technique everyone reaches for when they want a language model to be more reliable: run it a few times and take the majority answer. Self-consistency. Sample five, vote, ship the winner. It's in every "how to make your LLM more accurate" post. I measured it against the alternative on problems at the model's failure frontier, and the result was the opposite of the folklore: majority voting barely helped, and it scored worse than the single best reasoner. The thing that actually worked…
1Key Takeaways
- There's a technique everyone reaches for when they want a language model to be more reliable: run it a few times and take the majority answer.
- It's in every "how to make your LLM more accurate" post.
- I measured it against the alternative on problems at the model's failure frontier, and the result was the opposite of the folklore: majority voting barely helped, and it scored worse than the single best reasoner.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that there's a technique everyone reaches for when they want a language model to be more reliable: run it a few times and take the majority answer.
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