https://github.com/wenhuchen/HDSA-Dialog
공식 코드Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
Papers with Code 아카이브
Text Generation
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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:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wenhuchen/KGPT
공식 코드KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation
https://github.com/wenhuchen/LogicNLG
공식 코드https://github.com/wenlai-lavine/jola
공식 코드Joint Localization and Activation Editing for Low-Resource Fine-Tuning
https://github.com/wenlai-lavine/sneuron-tst
공식 코드Style-Specific Neurons for Steering LLMs in Text Style Transfer
https://github.com/wenliangdai/multi-task-offensive-language-detection
공식 코드Text generation for dataset augmentation in security classification tasks
https://github.com/wentinghome/amg
공식 코드Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots
https://github.com/wenxianxian/demvae
공식 코드Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
https://github.com/willemsenbram/reference-resolution-via-text-generation
공식 코드Resolving References in Visually-Grounded Dialogue via Text Generation
https://github.com/williamSYSU/CatGAN
공식 코드https://github.com/williamSYSU/TextGAN-PyTorch
공식 코드https://github.com/williamlyh/pos_div_metric
공식 코드https://github.com/williamyang1991/awesome-artistic-typography
공식 코드Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation
https://github.com/willxxy/ecg-byte
공식 코드ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
https://github.com/windylee0822/ctg
공식 코드Reinforcement Learning with Token-level Feedback for Controllable Text Generation
https://github.com/witerforcing/WriterForcing
공식 코드https://github.com/wjko2/Domain-Agnostic-Sentence-Specificity-Prediction
공식 코드https://github.com/wlr737/emnlp2022-seek
공식 코드Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection
https://github.com/wlr737/sibyl
공식 코드https://github.com/wlzhang2020/reasonrag
공식 코드Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
https://github.com/wolfgarbe/symspell
공식 코드An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
https://github.com/worldhellow/hallucinations-c2t
공식 코드https://github.com/wtliao/ImageTransformer
공식 코드https://github.com/wutong4012/Self-Consistent-Learning
공식 코드https://github.com/wuxingyu-ai/llm4ec
공식 코드Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
https://github.com/wwh0411/knowledgesg
공식 코드KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
https://github.com/wxh1996/lana-vln
공식 코드Lana: A Language-Capable Navigator for Instruction Following and Generation
https://github.com/wyu-du/controlled-dialogue-generation
공식 코드SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks
https://github.com/wyu-du/gp-vae
공식 코드Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors
https://github.com/wyu-du/self-training-dialogue-generation
공식 코드Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation
https://github.com/wyu97/InjType
공식 코드https://github.com/wyu97/KENLG-Reading
공식 코드https://github.com/xanderdavies/circuit-breaking
공식 코드Circuit Breaking: Removing Model Behaviors with Targeted Ablation
https://github.com/xbmxb/edit_analysis
공식 코드https://github.com/xdao85/vnhsge
공식 코드VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models
https://github.com/xdxie/WAS_WordArt-Segmentation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.