mlsanityData to 14 Oct 2026

Location-Aware Visual Question Generation with Lightweight Models

arXiv 2310.15129 ↗First posted 23 Oct 2023cs.CL cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that leverages GPT-4 to produce diverse and sophisticated questions. Then, we aim to learn a lightweight model that can address the LocaVQG task and fit on an edge device, such as a mobile phone. To this end, we propose a method which can reliably generate engaging questions from location-aware information. Our proposed method outperforms baselines regarding human evaluation (e.g., engagement, grounding, coherence) and automatic evaluation metrics (e.g., BERTScore, ROUGE-2). Moreover, we conduct extensive ablation studies to justify our proposed techniques for both generating the dataset and solving the task.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

23 of 41 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  5. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  6. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  7. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  8. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  9. 2022OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Frameworkcited by 745
  10. 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledgecited by 710
  11. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 642
  12. 2020MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devicescited by 482
11 more
  1. 2021Unifying Vision-and-Language Tasks via Text Generationcited by 412
  2. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 296
  3. 2021SituatedQA: Incorporating Extra-Linguistic Contexts into QAcited by 145
  4. 2021Scale Efficiently: Insights from Pre-training and Fine-tuning Transformerscited by 106
  5. 2023Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidancecited by 86
  6. 2020Grounded Situation Recognitioncited by 78
  7. 2016Generating Natural Questions About an Imagecited by 56
  8. 2021Your fairness may vary: Pretrained language model fairness in toxic text classificationcited by 39
  9. 2015A Survey of Current Datasets for Vision and Language Researchcited by 13
  10. 2019Distilling Knowledge Learned in BERT for Text Generationcited by 5
  11. 2022Multi-VQG: Generating Engaging Questions for Multiple Imagescited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learningcited by 14
  2. 2025GPS as a Control Signal for Image Generationcited by 5
  3. 2024Story Generation from Visual Inputs: Techniques, Related Tasks, and Challengescited by 3
  4. 2026Self-Evolving Visual Questionercited by 1