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/CharizardAcademy/modoc
공식 코드MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions
https://github.com/ChenRocks/Distill-BERT-Textgen
공식 코드https://github.com/ChenWu98/Coupled-VAE
공식 코드On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond
https://github.com/ChicagoHAI/HypoEval
공식 코드HypoEval: Hypothesis-Guided Evaluation for Natural Language Generation
https://github.com/ChunyuanLI/Optimus
공식 코드Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
https://github.com/Ciaranshu/relogic
공식 코드https://github.com/Crista23/JudgeTheJudges
공식 코드https://github.com/CyberAgentAILab/adaptive-mbr
공식 코드Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
https://github.com/CyberAgentAILab/diverse-mbr
공식 코드Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
https://github.com/CyberAgentAILab/model-based-mbr
공식 코드https://github.com/DRAGNLabs/towards-neural-programming-interfaces
공식 코드https://github.com/Dan123dan/rGBN-RNN
공식 코드Recurrent Hierarchical Topic-Guided RNN for Language Generation
https://github.com/DataScienceUIBK/PlausibleQA
공식 코드https://github.com/DataScienceUIBK/Rankify
공식 코드https://github.com/DataScienceUIBK/WikiHint
공식 코드WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation
https://github.com/DavyMorgan/llm-graph-probing
공식 코드https://github.com/DespairL/Personality
공식 코드Extroversion or Introversion? Controlling The Personality of Your Large Language Models
https://github.com/DhavalTaunk08/XWikiGen
공식 코드XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages
https://github.com/Diego999/GameWikiSum
공식 코드GameWikiSum: a Novel Large Multi-Document Summarization Dataset
https://github.com/Disiok/poetry-seq2seq
공식 코드Hierarchical Attention: What Really Counts in Various NLP Tasks
https://github.com/DonghoonKim-1938/VGD
공식 코드Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models
https://github.com/Douglas2Code/Img2Loc
공식 코드https://github.com/EagleW/ACL_titles_abstracts_dataset
공식 코드https://github.com/EagleW/Describing_a_Knowledge_Base
공식 코드https://github.com/EagleW/Multimedia-Generative-Script-Learning-for-Task-Planning
공식 코드https://github.com/EagleW/PaperRobot
공식 코드PaperRobot: Incremental Draft Generation of Scientific Ideas
https://github.com/EagleW/Stage-wise-Fine-tuning
공식 코드https://github.com/EdinburghNLP/wikigen
공식 코드https://github.com/EduardoTrevino/fail-talms
공식 코드https://github.com/Elbria/xformal-FoST
공식 코드XFORMAL: A Benchmark for Multilingual Formality Style Transfer
https://github.com/EnablerRx/GuidelineVulnDetect
공식 코드https://github.com/Euphoria16/DocMark
공식 코드Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding
https://github.com/Evan-Gao/Distractor-Generation-RACE
공식 코드Generating Distractors for Reading Comprehension Questions from Real Examinations
https://github.com/F-Yuan303/HELPD
공식 코드https://github.com/FCHXWH823/PrefixGPT
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.