Question Generation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

11 leaderboard tables shown for this task, 11 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 11 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

25 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 264 papers with code (664 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
  23 Oct 2019 57 repositories listed Syntology ran 2 of 31 samples · 29 unverified
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
• Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
  7 Oct 2016 25 repositories listed Syntology ran 7 of 14 samples · 7 unverified · 14 pointer-only (licence)
  We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
• Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
  7 Oct 2016 25 repositories listed Syntology ran 7 of 14 samples · 7 unverified · 14 pointer-only (licence)
  We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
• Learning to Ask: Neural Question Generation for Reading Comprehension
  29 Apr 2017 10 repositories listed
  We study automatic question generation for sentences from text passages in reading comprehension.
• Learning to Ask: Neural Question Generation for Reading Comprehension
  29 Apr 2017 10 repositories listed
  We study automatic question generation for sentences from text passages in reading comprehension.
• Unified Language Model Pre-training for Natural Language Understanding and Generation
  8 May 2019 9 repositories listed
  This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
• Unified Language Model Pre-training for Natural Language Understanding and Generation
  8 May 2019 9 repositories listed
  This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
• Neural Question Generation from Text: A Preliminary Study
  6 Apr 2017 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage.
• Neural Question Generation from Text: A Preliminary Study
  6 Apr 2017 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage.
• ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
  26 Jan 2020 5 repositories listed
  Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
• ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
  26 Jan 2020 5 repositories listed
  Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
• ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
  13 Jan 2020 5 repositories listed
  This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
• ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
  13 Jan 2020 5 repositories listed
  This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
• Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
  24 Oct 2024 4 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Despite the availability of several open-source multimodal datasets, limitations in the scale and quality of open-source instruction data hinder the performance of VLMs trained on these datasets, leading to a…
• Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
  24 Oct 2024 4 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Despite the availability of several open-source multimodal datasets, limitations in the scale and quality of open-source instruction data hinder the performance of VLMs trained on these datasets, leading to a…
• Learning Dense Representations of Phrases at Scale
  23 Dec 2020 4 repositories listed
  Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).
• Simplifying Paragraph-level Question Generation via Transformer Language Models
  3 May 2020 4 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  Question generation (QG) is a natural language generation task where a model is trained to ask questions corresponding to some input text.
• Simplifying Paragraph-level Question Generation via Transformer Language Models
  3 May 2020 4 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  Question generation (QG) is a natural language generation task where a model is trained to ask questions corresponding to some input text.
• Synthetic QA Corpora Generation with Roundtrip Consistency
  12 Jun 2019 4 repositories listed
  We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency.
• Synthetic QA Corpora Generation with Roundtrip Consistency
  12 Jun 2019 4 repositories listed
  We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency.
• Machine Comprehension by Text-to-Text Neural Question Generation
  4 May 2017 4 repositories listed
  We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers.
• Machine Comprehension by Text-to-Text Neural Question Generation
  4 May 2017 4 repositories listed
  We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers.
• IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
  7 Mar 2022 3 repositories listed
  We introduce IT5, the first family of encoder-decoder transformer models pretrained specifically on Italian.
• TruthfulQA: Measuring How Models Mimic Human Falsehoods
  8 Sep 2021 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  We crafted questions that some humans would answer falsely due to a false belief or misconception.
• ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
  16 Apr 2021 3 repositories listed
  ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
• ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
  16 Apr 2021 3 repositories listed
  ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
• ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
  1 Nov 2020 3 repositories listed
  This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
• ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
  1 Nov 2020 3 repositories listed
  This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
• Investigating Pretrained Language Models for Graph-to-Text Generation
  16 Jul 2020 3 repositories listed Syntology ran 2 of 12 samples · 10 unverified
  We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
• UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
  28 Feb 2020 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).

Syntology lines on 12 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.