# QOG:Question and Options Generation based on Language Model

arXiv: https://arxiv.org/abs/2406.12381 · HTML: https://mlsanity.com/paper/2406.12381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 35 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Synthetic QA Corpora Generation with Roundtrip Consistency](https://mlsanity.com/paper/1906.05416/) (2019)
- [Training Question Answering Models From Synthetic Data](https://mlsanity.com/paper/2002.09599/) (2020)
- [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://mlsanity.com/paper/2104.08678/) (2021)
- [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://mlsanity.com/paper/1909.06356/) (2019)
- [Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering](https://mlsanity.com/paper/2010.12643/) (2020)
- [XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering](https://mlsanity.com/paper/1905.11471/) (2019)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methods](https://mlsanity.com/paper/2307.08161/) (2023)
- [An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering](https://mlsanity.com/paper/1912.02145/) (2019)
- [Asking It All: Generating Contextualized Questions for any Semantic Role](https://mlsanity.com/paper/2109.04832/) (2021)
- [PaniniQA: Enhancing Patient Education Through Interactive Question Answering](https://mlsanity.com/paper/2308.03253/) (2023)
- [Local Self-Attention over Long Text for Efficient Document Retrieval](https://mlsanity.com/paper/2005.04908/) (2020)
- [An Empirical Comparison of LM-based Question and Answer Generation Methods](https://mlsanity.com/paper/2305.17002/) (2023)
- [Accelerating Real-Time Question Answering via Question Generation](https://mlsanity.com/paper/2009.05167/) (2020)
- [EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping](https://mlsanity.com/paper/1911.05013/) (2019)
- [QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation](https://mlsanity.com/paper/2309.10326/) (2023)

## Built on by (most cited first, 1 of 1)

- [Grounding Synthetic Data Evaluations of Language Models in Unsupervised Document Corpora](https://mlsanity.com/paper/2505.08905/) (2025)

## Abstract (arXiv)

Question-Options Generation (QOG) is a task that involves generating a set of question-options pairs given context. This task has various applications, including fine-tuning large models, information retrieval, and automated multiple-choice question generation for education. In this paper, we develop QOG models using three different methods based on fine-tuning sequence-to-sequence language models (LMs). Experiments demonstrate that the end-to-end QOG model is computationally efficient and stable during both training and inference, outperforming other methods. Furthermore, our analysis indicates that our QOG models are competitive on the QOG task compared to the large language model Llama 3-8B.