most citedMagnitude Matters: a Superior Class of Similarity Metrics for Holistic Semantic Understanding

2 citations · 2 across the 3 of their papers we have counts for

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3 papers

cs.CL2025

Confidence, Not Perplexity: A Better Metric for the Creative Era of LLMs

V. S. Raghu Parupudi

Reference-free metrics like self-perplexity are strongly biased against creative text generation. We propose the Confidence Score (CS), derived from a model's output probability di…

cs.CL2025

Systematic Diagnosis of Brittle Reasoning in Large Language Models

V. S. Raghu Parupudi

A central question in artificial intelligence is the extent to which machine learning models comprehend mathematics. To address this, we propose a novel framework for measuring mat…

cs.CL2025★ 2 cited

Magnitude Matters: a Superior Class of Similarity Metrics for Holistic Semantic Understanding

V. S. Raghu Parupudi

Vector comparison in high dimensions is a fundamental task in NLP, yet it is dominated by two baselines: the raw dot product, which is unbounded and sensitive to vector norms, and…