Text Generation

Description from the archive archive 2025-07-28; Papers-with-Code links inside it are rewritten to this site.

Benchmarks archive 2025-07-28

155 leaderboard tables shown for this task, 20 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 155 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

162 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 162 until expanded.

Subtasks archive 2025-07-28

25 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 2,047 papers with code (5,335 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.

• Learning Transferable Visual Models From Natural Language Supervision
  26 Feb 2021 82 repositories listed Syntology ran 16 of 20 samples · 4 unverified · 16 pointer-only (licence)
  State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
• Show and Tell: A Neural Image Caption Generator
  17 Nov 2014 74 repositories listed Syntology ran 13 of 34 samples · 21 unverified · 6 pointer-only (licence)
  Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions.
• Generating Sequences With Recurrent Neural Networks
  4 Aug 2013 59 repositories listed Syntology ran 7 of 37 samples · 30 unverified · 4 pointer-only (licence)
  This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time.
• BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
  29 Oct 2019 47 repositories listed Syntology ran 22 of 53 samples · 31 unverified · 7 pointer-only (licence)
  We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single…
• 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.
• SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
  18 Sep 2016 23 repositories listed Syntology ran 10 of 21 samples · 11 unverified · 13 pointer-only (licence)
  As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued…
• Language Models are Unsupervised Multitask Learners
  14 Feb 2019 21 repositories listed
  Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
• BERTScore: Evaluating Text Generation with BERT
  21 Apr 2019 20 repositories listed Syntology ran 44 of 76 samples · 32 unverified · 33 pointer-only (licence)
  We propose BERTScore, an automatic evaluation metric for text generation.
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
  22 May 2020 18 repositories listed Syntology ran 4 of 6 samples · 2 unverified
  Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
  30 Jan 2023 17 repositories listed Syntology ran 4 of 8 samples · 4 unverified · 1 pointer-only (licence)
  The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models.
• Prefix-Tuning: Optimizing Continuous Prompts for Generation
  1 Jan 2021 13 repositories listed Syntology ran 4 of 6 samples · 2 unverified
  Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks.
• HuggingFace's Transformers: State-of-the-art Natural Language Processing
  9 Oct 2019 9 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of 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.
• LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
  20 Mar 2024 8 repositories listed Syntology ran 3 of 17 samples · 14 unverified
  Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
• TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
  21 Sep 2021 8 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Text recognition is a long-standing research problem for document digitalization.
• CTRL: A Conditional Transformer Language Model for Controllable Generation
  11 Sep 2019 8 repositories listed Syntology ran 13 of 14 samples · 1 unverified
  Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text.
• NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
  15 May 2023 7 repositories listed
  Our proposed framework provides access: (i) for verifying whether automatic metrics are faithful to human preference, regardless of their correlation level to human; and (ii) for inspecting the strengths and limitations…
• Plug and Play Language Models: A Simple Approach to Controlled Text Generation
  4 Dec 2019 7 repositories listed Syntology ran 4 of 22 samples · 18 unverified
  Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
• MASS: Masked Sequence to Sequence Pre-training for Language Generation
  7 May 2019 7 repositories listed
  Pre-training and fine-tuning, e.
• Qwen2.5 Technical Report
  19 Dec 2024 6 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2.
• Structured Denoising Diffusion Models in Discrete State-Spaces
  7 Jul 2021 6 repositories listed Syntology ran 5 of 5 samples · 0 unverified
  Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al.
• Neural Text Generation with Unlikelihood Training
  12 Aug 2019 6 repositories listed Syntology ran 6 of 6 samples · 0 unverified · 4 pointer-only (licence)
  Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core.
• GLTR: Statistical Detection and Visualization of Generated Text
  10 Jun 2019 6 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  The rapid improvement of language models has raised the specter of abuse of text generation systems.
• Training language GANs from Scratch
  23 May 2019 6 repositories listed
  Generative Adversarial Networks (GANs) enjoy great success at image generation, but have proven difficult to train in the domain of natural language.
• fairseq: A Fast, Extensible Toolkit for Sequence Modeling
  1 Apr 2019 6 repositories listed Syntology ran 1 of 11 samples · 10 unverified · 5 pointer-only (licence)
  fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks.
• Long Text Generation via Adversarial Training with Leaked Information
  24 Sep 2017 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.
• Geometric GAN
  8 May 2017 6 repositories listed
  Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other.
• A Syntactic Neural Model for General-Purpose Code Generation
  6 Apr 2017 6 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python.
• Boundary-Seeking Generative Adversarial Networks
  27 Feb 2017 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for…
• Sequence-to-Sequence Learning as Beam-Search Optimization
  9 Jun 2016 6 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  In this work, we introduce a model and beam-search training scheme, based on the work of Daume III and Marcu (2005), that extends seq2seq to learn global sequence scores.

Syntology lines on 24 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.