AI Research
A decodability criterion predicts when hidden-state selection beats majority voting in large language models
New research introduces decodability to optimize how LLMs process information and select accurate answers.
Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by majority voting. Voting is unreliable on difficult questions, where the sampled answers share correlated errors, so the wrong a...
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ArXiv CS.AI
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ArXiv CS.AI
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This research introduces a reliable metric called decodability to determine when internal model signals outperform traditional majority voting for LLM decision-making. By identifying precisely when hidden-state analysis is more effective, this method optimizes accuracy for complex, high-stakes tasks.
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