πŸ“„
Automatic Generation of Multiple-Choice Questions paper by Independent / Community
⭐ 65.4
πŸ“„
Paper

Automatic Generation of Multiple-Choice Questions

by Independent / Community 02eabbf006f83aa8b80697cfab0f8dc808094c12
Free2AITools Nexus Index
65.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 76
P: Popularity 51
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) and adequate distractors. We present two methods to tackle the challenge of QAP generations: (1) A deep-learning-based end-to-end question generation system based on T5 Transformer with Preprocessing and Postprocessing Pipelines (TP3). We use the finetuned T5 model for our downstream task of question generation and improve accuracy using a combination of vario...

Source β†’
Semantic Scholar 10 Citations
Paper Information Summary
Entity Passport
Registry ID 02eabbf006f83aa8b80697cfab0f8dc808094c12
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{02eabbf006f83aa8b80697cfab0f8dc808094c12,
  author = {Unknown},
  title = {Automatic Generation of Multiple-Choice Questions Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/02eabbf006f83aa8b80697cfab0f8dc808094c12}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Automatic Generation of Multiple-Choice Questions [Paper]. Free2AITools. https://api.semanticscholar.org/02eabbf006f83aa8b80697cfab0f8dc808094c12

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 76
Popularity (P) 51
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Automatic Generation of Multiple-Choice Questions: Authority (A:76), Popularity (P:51), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“ Executive Summary

"Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) and adequate distractors. We present two methods to tackle the challenge of QAP generations: (1) A deep-learning-based end-to-end question generation system based on T5 Transformer with Preprocessing and Postprocessing Pipelines (TP3). We use the finetuned T5 model for our downstream task of question generation and improve accuracy using a combination of vario..."

❝ Cite Node 

@article{Unknown2026Automatic,
  title={Automatic Generation of Multiple-Choice Questions},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ10CitationsSemantic Scholar
πŸ›οΈ76AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈautomation workflowField

🏷️ Research Topics

fine tuningtransformer architecture
πŸ“¦Data Source: semantic_scholar
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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

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0
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citations
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