Hugging Face Trending Papers
Frustratingly Easy Majority Voting Using Length Conditioning
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arXiv Computation and Language
Oct 15
Frustratingly Easy Majority Voting Using Length Conditioning
The paper introduces Length‑Conditioned Majority Voting (LCMV), a test‑time training method that enhances the standard Majority Voting (MV) technique for Large Language Models (LLMs). LCMV incorporates an empirical bias that longer Chain‑of‑Thoughts (CoTs) are less likely to be correct by modeling a length‑conditioned prior with a logistic function. This prior is adapted on unlabeled test data without fine‑tuning the LLM, and the resulting weighted votes consistently outperform standard MV and other length‑ or confidence‑based aggregation strategies across multiple reasoning benchmarks.
arXiv AI
Aug 18
Funnel of Thoughts: Efficient Test-Time Scaling via Early Voting and Rollout Pruning
arXiv:2608. 15065v1 Announce Type: new Abstract: Large Reasoning Models produce diverse, sometimes inconsistent answers across repeated queries on the same problem, so multi-sample inference is a prerequisite for reliable deployment.
arXiv AI
Jun 3
Distribution-Calibrated Inference Time Compute for Thinking LLM-as-a-Judge
arXiv:2512. 03019v2 Announce Type: replace-cross Abstract: Thinking Large Language Models (LLMs) used as judges for pairwise preferences remain noisy at the single-sample level, and common aggregation rules (majority vote, soft self-consistency, or instruction-based self-aggregation) are inconsistent when ties are allowed.
arXiv AI
Oct 15
Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling
arXiv:2610.18583v1 Announce Type: new Abstract: Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequen...
arXiv AI
Jul 1
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
arXiv:2511. 12309v2 Announce Type: replace-cross Abstract: Self-consistency (SC) is a widely used test-time inference technique for improving performance in chain-of-thought reasoning.
arXiv AI
Sep 24
Count Evidence, Not Sentences: Tempered Evidence Fusion of LLM Judgments for Long-Text Value Measurement
arXiv:2609.27165v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to measure public value orientations from long social media posts, yet such posts often mix backgr...