2 citations · 2 across the 5 of their papers we have counts for
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Anisotropy Decides Cosine vs. Rank Metrics for Text Embeddings
V. S. Raghu Parupudi
The standard way to compare two text embeddings is cosine similarity. Scattered studies report that a different metric does better, but never pin down the geometric condition that…
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…
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…
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…