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Understanding Shannon's Entropy metric for Information

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arxiv 1405.2061 v1 pith:J4E5MKIB submitted 2014-03-24 cs.IT math.IT

Sriram Vajapeyam This is my paper · ORCID
classification cs.ITmath.IT
keywords informationentropymetricshannonunderstandingarticleconceptfoundational
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Shannon's metric of "Entropy" of information is a foundational concept of information theory. This article is a primer for novices that presents an intuitive way of understanding, remembering, and/or reconstructing Shannon's Entropy metric for information.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions

    cs.CL 2025-06 conditional novelty 6.0 of 10

    BioMol-MQA is a new multimodal QA dataset for polypharmacy in which LLMs perform poorly zero-shot but much better when given gold context.

  2. Cross-Attention Calibrated Deduplication for Retrieval-Augmented Generation System

    cs.CL 2026-07 conditional novelty 5.5 of 10

    CACD deduplicates RAG chunks via cross-encoder scores, attention-entropy NIS, and majority vote, dropping ~9.75% of chunks on SQuAD faster than cosine filtering.

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