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

Source: [SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering](<https://www.sota2.com/research/paper/68fa760b1e209131cea6b14f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:46:34.345Z

Authors: Zhu Yufan, Hao Zeyu, Li Siqi, Niu Boqian

Published: 2024-12-07T02:01:27.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.05499>)

[PDF](<https://arxiv.org/pdf/2412.05499.pdf>)