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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 5201–5250 of 5335 papers

TitleStatusHype
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation—0
Low Level Linguistic Controls for Style Transfer and Content Preservation—0
LSTM-Based Text Generation: A Study on Historical Datasets—0
LSTM Hypertagging—0
LVMed-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation—0
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods—0
Machine Learning of Phonologically Conditioned Noun Declensions For Tamil Morphological Generators—0
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots—0
Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing—0
Machine Translation Pre-training for Data-to-Text Generation -- A Case Study in Czech—0
Machine Translation Pre-training for Data-to-Text Generation - A Case Study in Czech—0
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection—0
Macroscopic Control of Text Generation for Image Captioning—0
Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It's Hard for Computers—0
MAGNET: Augmenting Generative Decoders with Representation Learning and Infilling Capabilities—0
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior—0
Maintaining Informative Coherence: Migrating Hallucinations in Large Language Models via Absorbing Markov Chains—0
Make Large Language Model a Better Ranker—0
Making Large Language Models A Better Foundation For Dense Retrieval—0
Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding—0
Making the Most of your Model: Methods for Finetuning and Applying Pretrained Transformers—0
Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training—0
MALA: Cross-Domain Dialogue Generation with Action Learning—0
MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection—0
MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning—0
MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities—0
Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal—0
Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences—0
Mapping the Design Space of Interactions in Human-AI Text Co-creation Tasks—0
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy—0
Marathi-English Code-mixed Text Generation—0
Mark-Evaluate: Assessing Language Generation using Population Estimation Methods—0
Markov Constraint as Large Language Model Surrogate—0
Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling—0
MaskGAN: Better Text Generation via Filling in the______—0
MaskGAN: Better Text Generation via Filling in the _______—0
Massive-scale Decoding for Text Generation using Lattices—0
Mastering Board Games by External and Internal Planning with Language Models—0
Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defer—0
MatChat: A Large Language Model and Application Service Platform for Materials Science—0
P^2: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation—0
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning—0
Maximizing Signal in Human-Model Preference Alignment—0
May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation—0
Exploring the Maze of Multilingual Modeling—0
MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods—0
MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels—0
Meaningful Answer Generation of E-Commerce Question-Answering—0
Measuring Misogyny in Natural Language Generation: Preliminary Results from a Case Study on two Reddit Communities—0
Measuring Patent Claim Generation by Span Relevancy—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