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SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering

arXiv 2412.05499 ↗First posted 7 Dec 2024cs.CL cs.LGcited by 0

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Abstract (arXiv)

SplaXBERT, built on ALBERT-xlarge with context-splitting and mixed precision training, achieves high efficiency in question-answering tasks on lengthy texts. Tested on SQuAD v1.1, it attains an Exact Match of 85.95% and an F1 Score of 92.97%, outperforming traditional BERT-based models in both accuracy and resource efficiency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 9 references that are in the map. First posted on arXiv 7 Dec 2024.

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References · 5

  1. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  2. 2017Mixed Precision Trainingcited by 1,156
  3. 2015Highway Networkscited by 260
  4. 2021Ensemble ALBERT on SQuAD 2.0cited by 4
  5. 2020Comparative Study of Machine Learning Models and BERT on SQuADcited by 1

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