# Clues Before Answers: Generation-Enhanced Multiple-Choice QA

arXiv: https://arxiv.org/abs/2205.00274 · HTML: https://mlsanity.com/paper/2205.00274/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 47 references that are in the map.
- First posted on arXiv 30 Apr 2022.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [UnifiedQA: Crossing Format Boundaries With a Single QA System](https://mlsanity.com/paper/2005.00700/) (2020)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [QASC: A Dataset for Question Answering via Sentence Composition](https://mlsanity.com/paper/1910.11473/) (2019)
- [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://mlsanity.com/paper/2002.04326/) (2020)
- [Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning](https://mlsanity.com/paper/2010.15875/) (2020)
- [Generation-Augmented Retrieval for Open-domain Question Answering](https://mlsanity.com/paper/2009.08553/) (2020)
- [Unsupervised Commonsense Question Answering with Self-Talk](https://mlsanity.com/paper/2004.05483/) (2020)
- [CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation](https://mlsanity.com/paper/2109.05729/) (2021)
- [Pre-training Text-to-Text Transformers for Concept-centric Common Sense](https://mlsanity.com/paper/2011.07956/) (2020)
- [Explaining Question Answering Models through Text Generation](https://mlsanity.com/paper/2004.05569/) (2020)
- [Dual Co-Matching Network for Multi-choice Reading Comprehension](https://mlsanity.com/paper/1901.09381/) (2019)
- [RikiNet: Reading Wikipedia Pages for Natural Question Answering](https://mlsanity.com/paper/2004.14560/) (2020)
- [TSQA: Tabular Scenario Based Question Answering](https://mlsanity.com/paper/2101.11429/) (2021)
- [Improving Machine Reading Comprehension with General Reading Strategies](https://mlsanity.com/paper/1810.13441/) (2018)
- [GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level](https://mlsanity.com/paper/1908.07855/) (2019)
- [Option Comparison Network for Multiple-choice Reading Comprehension](https://mlsanity.com/paper/1903.03033/) (2019)
- [AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension](https://mlsanity.com/paper/2203.08992/) (2022)

## Built on by (most cited first, 9 of 9)

- [KnowGPT: Knowledge Graph based Prompting for Large Language Models](https://mlsanity.com/paper/2312.06185/) (2023)
- [Elaboration-Generating Commonsense Question Answering at Scale](https://mlsanity.com/paper/2209.01232/) (2022)
- [Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering](https://mlsanity.com/paper/2305.08135/) (2023)
- [XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.15529/) (2024)
- [PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs](https://mlsanity.com/paper/2401.17536/) (2024)
- [Pipelined Decoder for Efficient Context-Aware Text Generation](https://mlsanity.com/paper/2506.23431/) (2025)
- [Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture](https://mlsanity.com/paper/2303.16753/) (2023)
- [Differentiating Choices via Commonality for Multiple-Choice Question Answering](https://mlsanity.com/paper/2408.11554/) (2024)
- [Internal and External Knowledge Interactive Refinement Framework for Knowledge-Intensive Question Answering](https://mlsanity.com/paper/2408.12979/) (2024)

## Abstract (arXiv)

A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal. However, a side effect of twisting a generation target to fit the classification nature of MCQA is the under-utilization of the decoder and the knowledge that can be decoded. To exploit the generation capability and underlying knowledge of a pre-trained encoder-decoder model, in this paper, we propose a generation-enhanced MCQA model named GenMC. It generates a clue from the question and then leverages the clue to enhance a reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.