score-audio-cots-grounding-coherence-causality TEC-0193 · proposedScore audio CoTs on grounding, coherence and causality
Prompts the model with a four-step Perception/Analysis/Reasoning/Answer template, then scores the chain with six automatic metrics (CLAP audio-entity grounding, inter-step embedding coherence, small-LM substantiveness, perplexity-based step
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Prompts the model with a four-step Perception/Analysis/Reasoning/Answer template, then scores the chain with six automatic metrics (CLAP audio-entity grounding, inter-step embedding coherence, small-LM substantiveness, perplexity-based step Filed by the drafting stage from Beyond Accuracy: ARIA-Rubrics for Evaluating Audio Reasoning in Large Audio Language Models (2609.09681) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: The six-metric mean correlated with 3-rater human CoT quality ratings at Spearman 0.671 (p=0.006) over 450 sampled instances, above every single metric (best M1, 0.563) and every two-metric subgroup; rankings also matched two LLM judges and CoT-perturbation-derived mode assignments, and were stable across datasets (model identity explained 91.9-96.4% of variance) and across scorer backbones.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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