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 4851–4900 of 5335 papers

TitleStatusHype
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation—0
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation—0
"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation—0
Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection—0
Imitation learning for language generation from unaligned data—0
Imitation learning for structured prediction in natural language processing—0
Immersive Text Game and Personality Classification—0
Immunization against harmful fine-tuning attacks—0
Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation—0
Implicit Bias in LLMs: A Survey—0
Implicit Kernel Learning—0
Important Attribute Identification in Knowledge Graph—0
(Im)possibility of Automated Hallucination Detection in Large Language Models—0
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization—0
Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder—0
Improved Natural Language Generation via Loss Truncation—0
Improved Training of Mixture-of-Experts Language GANs—0
Improved Unbiased Watermark for Large Language Models—0
Improved Variational Neural Machine Translation by Promoting Mutual Information—0
Improvement in Machine Translation with Generative Adversarial Networks—0
Improving Abstraction in Text Summarization—0
Improving Abstractive Summarization with Commonsense Knowledge—0
Improving Adversarial Text Generation by Modeling the Distant Future—0
Improving Adversarial Text Generation with n-Gram Matching—0
Improving Attributed Text Generation of Large Language Models via Preference Learning—0
Improving Autoregressive Training with Dynamic Oracles—0
Improving Citation Text Generation: Overcoming Limitations in Length Control—0
Improving Code-switched ASR with Linguistic Information—0
Improving Compositional Generalization with Self-Training for Data-to-Text Generation—0
Improving Compositional Generalization with Self-Training for Data-to-Text Generation—0
Improving Controllable Text Generation with Position-Aware Weighted Decoding—0
Improving Controllable Text Generation with Position-Aware Weighted Decoding—0
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach—0
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance—0
Improving Diversity of Neural Text Generation via Inverse Probability Weighting—0
Improving Emotional Expression and Cohesion in Image-Based Playlist Description and Music Topics: A Continuous Parameterization Approach—0
Improving Explainable Recommendations with Synthetic Reviews—0
Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs—0
Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation—0
Improving Factuality with Explicit Working Memory—0
Improving Instruction-Following in Language Models through Activation Steering—0
Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders—0
Improving Logical-Level Natural Language Generation with Topic-Conditioned Data Augmentation and Logical Form Generation—0
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation—0
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling—0
Improving Neural Abstractive Document Summarization with Structural Regularization—0
Improving Neural Language Generation with Spectrum Control—0
Improving Neutral Point of View Text Generation through Parameter-Efficient Reinforcement Learning and a Small-Scale High-Quality Dataset—0
Improving N-gram Language Models with Pre-trained Deep Transformer—0
Improving Shared Argument Identification in Japanese Event Knowledge Acquisition—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