Summary of Splaxbert: Leveraging Mixed Precision Training and Context Splitting For Question Answering, by Zhu Yufan et al.
SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering
by Zhu Yufan, Hao Zeyu, Li Siqi, Niu Boqian
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel AI model called SplaXBERT is introduced, which leverages the ALBERT-xlarge architecture with context-splitting and mixed precision training to efficiently answer questions on lengthy texts. The model demonstrates exceptional performance on question-answering tasks, achieving an Exact Match of 85.95% and an F1 Score of 92.97% on the SQuAD v1.1 dataset. These results surpass traditional BERT-based models in both accuracy and resource efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SplaXBERT is a new AI model that can quickly answer questions about long pieces of text. It works by splitting up the context and using special training methods to be more efficient. The model did very well on a test called SQuAD, answering 85.95% of questions correctly and getting 92.97% of answers right according to another measure. This is better than other similar models in both how well it does and how much computer power it uses. |
Keywords
» Artificial intelligence » Bert » F1 score » Precision » Question answering
