Long-Tail Knowledge in Large Language Models: Taxonomy, Mechanisms, Interventions and Implications
2026/02/18 by Sanket Badhe, Deep Shah, Dipti Shah +1 · 1 voice
Computer Science · Medicine · Social Sciences · #Accountability #Artificial Intelligence in Healthcare and Education #Computational and Text Analysis Methods #Corporate governance #Knowledge production #Language model #Psychological intervention #Sociotechnical system #Taxonomy (biology) #Topic Modeling #Work (physics) #cs.AI #cs.CL #cs.CY
paper · pdf · doi:10.48550/arxiv.2602.16201
openalex publication_date 2026/02/18 · arxiv published 2026/02/18 · arxiv updated 2026/02/18 · openalex created_date 2026/02/20 · openalex updated_date 2026/07/28
Abstract
Large language models (LLMs) are trained on web-scale corpora that exhibit steep power-law distributions, in which the distribution of knowledge is highly long-tailed, with most appearing infrequently. While scaling has improved average-case performance, persistent failures on low-frequency, domain-specific, cultural, and temporal knowledge remain poorly characterized. This paper develops a structured taxonomy and analysis of long-Tail Knowledge in large language models, synthesizing prior work across technical and sociotechnical perspectives. We introduce a structured analytical framework that synthesizes prior work across four complementary axes: how long-Tail Knowledge is defined, the mechanisms by which it is lost or distorted during training and inference, the technical interventions proposed to mitigate these failures, and the implications of these failures for fairness, accountability, transparency, and user trust. We further examine how existing evaluation practices obscure tail behavior and complicate accountability for rare but consequential failures. The paper concludes by identifying open challenges related to privacy, sustainability, and governance that constrain long-Tail Knowledge representation. Taken together, this paper provides a unifying conceptual framework for understanding how long-Tail Knowledge is defined, lost, evaluated, and manifested in deployed language model systems.
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