arXiv:2607. 20464v1 Announce Type: new Abstract: When a language model gives different answers on repeated runs, does that variation reveal what it does not know?
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The study investigates why language models sometimes give consistent errors across related questions. By analyzing the correlation matrix of model responses, it finds that within a single model, independently drawn answers show no structure, but structure emerges when all questions share a random seed or few‑shot prompt. Across 24 models, the strongest additional structure is a preference for specific answer letters, especially in Llama‑3.2‑1B, and removing this effect leaves only a small excess of errors linked to a few model pairs.
arXiv:2502. 11027v5 Announce Type: replace Abstract: Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it.
arXiv:2609. 16933v1 Announce Type: cross Abstract: Modern predictive systems expose multiple quantities that are commonly interpreted as measures of confidence.
arXiv:2608. 11403v1 Announce Type: new Abstract: Self-consistency (SC) via majority vote is a widely used way to spend inference-time compute: sample N chains of thought, return the plurality answer.
arXiv:2610. 21363v1 Announce Type: cross Abstract: Test-time scaling can improve the performance of large language models by generating multiple answers to a question and aggregating them.
arXiv:2609.39560v1 Announce Type: new Abstract: Sampling several responses and voting over their answers can improve a language model's accuracy, but repeated answers limit the benefit of additional...