SOTAVerified

Text Generation

Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.

Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.

Further readings:

( Image credit: Adversarial Ranking for Language Generation )

Papers

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Showing 2201–2250 of 5335 papers

TitleStatusHype
Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations—0
Humor Generation and Detection in Code-Mixed Hindi-English—0
Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder—0
Décodage guidé par un discriminateur avec le Monte Carlo Tree Search pour la génération de texte contrainte (Discriminator-guided decoding with Monte Carlo Tree Search for constrained text generation )—0
Backtracking Improves Generation Safety—0
HumT DumT: Measuring and controlling human-like language in LLMs—0
DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation—0
Debias your Large Multi-Modal Model at Test-Time with Non-Contrastive Visual Attribute Steering—0
A Natural Language Instructor for pedestrian navigation based in generation by selection—0
Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers—0
Hybrid Semantics for Goal-Directed Natural Language Generation—0
Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations—0
Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo—0
De-Biased Court's View Generation with Causality—0
Backbone Augmented Training for Adaptations—0
A Natural Bias for Language Generation Models—0
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation—0
De-Anonymizing Text by Fingerprinting Language Generation—0
DATScore: Evaluating Translation with Data Augmented Translations—0
AXCEL: Automated eXplainable Consistency Evaluation using LLMs—0
A working, non-trivial, topically indifferent NLG System for 17 languages—0
Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts—0
Human Perception in Natural Language Generation—0
Avoiding Overlap in Data Augmentation for AMR-to-Text Generation—0
Human-like Natural Language Generation Using Monte Carlo Tree Search—0
Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers—0
An Assessment of ChatGPT on Log Data—0
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors—0
DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articles—0
AUTOSUMM: Automatic Model Creation for Text Summarization—0
Improving Neural Machine Translation with Pre-trained Representation—0
Human or Machine: Automating Human Likeliness Evaluation of NLG Texts—0
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications—0
IGOT: Information Gain Optimized Tokenizer on Domain Adaptive Pretraining—0
Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder—0
Including Facial Expressions in Contextual Embeddings for Sign Language Generation—0
Data-Driven News Generation for Automated Journalism—0
Auto-regressive Text Generation with Pre-Trained Language Models: An Empirical Study on Question-type Short Text Generation—0
Data-driven Natural Language Generation: Paving the Road to Success—0
An Application of Pseudo-Log-Likelihoods to Natural Language Scoring—0
How to Tell a Schneemann from a Milchmann: An Annotation Scheme for Compound-Internal Relations—0
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)—0
How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue—0
Data-dependent Gaussian Prior Objective for Language Generation—0
Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study—0
An Annotated Corpus of Film Dialogue for Learning and Characterizing Character Style—0
How Useful are Educational Questions Generated by Large Language Models?—0
Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers—0
Data Augmentation for Visual Question Answering—0
Data Augmentation for Text Generation Without Any Augmented Data—0
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All datasetsDARTCOCO CaptionsEMNLP2017 WMTReDialCommonGenROCStoriesChinese PoemsCzech restaurant informationOpenWebTextSciQYahoo QuestionsADGEN

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1T5B BaselineBLEU48.74—Unverified
2FactT5BBLEU48.37—Unverified
3JointGT BaselineBLEU47.51—Unverified
4FactJointGTBLEU47.39—Unverified
5Control Prefixes (T5-large)METEOR0.41—Unverified
6T5METEOR0.12—Unverified
7BARTMETEOR0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1LeakGANBLEU-20.95—Unverified
2partGANBLEU-20.91—Unverified
3RankGANBLEU-20.85—Unverified
4RelGAN (100)BLEU-20.85—Unverified
5SeqGANBLEU-20.83—Unverified
#ModelMetricClaimedVerifiedStatus
1LeakGANBLEU-20.96—Unverified
2PPOGANBLEU-20.91—Unverified
3RelGANBLEU-20.88—Unverified
4SeqGANBLEU-20.86—Unverified
5RankGANBLEU-20.78—Unverified
#ModelMetricClaimedVerifiedStatus
1UniCRSDistinct-30.65—Unverified
2CRFRDistinct-30.52—Unverified
3KGSFDistinct-30.43—Unverified
4C2CRSDistinct-30.33—Unverified
5KBRDDistinct-30.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UniLMCIDEr14.92—Unverified
2BART (TextBox 2.0)CIDEr12.98—Unverified
3BARTMETEOR0.3—Unverified
4T5METEOR0.29—Unverified
#ModelMetricClaimedVerifiedStatus
1Beam search + A*esque (beam)BLEU-134.4—Unverified
2Beam search + A*esque (sample)BLEU-134.4—Unverified
3Beam search + A*esque (greedy)BLEU-134.3—Unverified
4Beam searchBLEU-133.7—Unverified
#ModelMetricClaimedVerifiedStatus
1RankGANBLEU-20.81—Unverified
2SeqGANBLEU-20.74—Unverified
3LeakGANBLEU-20.46—Unverified
#ModelMetricClaimedVerifiedStatus
1TGen++METEOR0.17—Unverified
2TGenMETEOR0.15—Unverified
3TGen+METEOR0.15—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT2-124Meval_loss3.12—Unverified
2GPT2-81M-LOOPeval_loss3.11—Unverified
3GPT2-Hermiteeval_loss2.91—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA-65B+CFG (zero-shot)Accuracy96.6—Unverified
2LLaMA-30B+CFG (zero-shot)Accuracy96.4—Unverified
3LLaMA-13B+CFG (zero-shot)Accuracy95.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN-VAENLL332.1—Unverified
2SA-VAENLL327.5—Unverified
3Aggressive VAENLL326.7—Unverified
#ModelMetricClaimedVerifiedStatus
1BART (TextBox 2.0)BLEU-410.2—Unverified
#ModelMetricClaimedVerifiedStatus
1STWGAN-GPBLEU-30.62—Unverified
#ModelMetricClaimedVerifiedStatus
1PALMROUGE-L41.41—Unverified
#ModelMetricClaimedVerifiedStatus
1BART (TextBox 2.0)ROUGE-L64.34—Unverified
#ModelMetricClaimedVerifiedStatus
1AEM+AttentionBLEU-114.17—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4ASR65.1—Unverified
#ModelMetricClaimedVerifiedStatus
1BART (TextBox 2.0)ROUGE-L42.96—Unverified
#ModelMetricClaimedVerifiedStatus
1Graph2SeqBLEU22—Unverified
#ModelMetricClaimedVerifiedStatus
1WGANGP + DGflowJS-40.19—Unverified