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/john-bradshaw/rxn-lm
https://github.com/jolinxql/LSTM
https://github.com/jonathanmli/avalon-llm
Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search
https://github.com/jongwooko/distillm
https://github.com/jongwooko/nash-pruning-official
https://github.com/joongbo/tta
https://github.com/joytianya/google_bert
https://github.com/jpark621/language-style-transfer
https://github.com/jparkhill/TensorMol
https://github.com/jpwahle/emnlp23-citation-field-influence
How Large Language Models are Transforming Machine-Paraphrased Plagiarism
https://github.com/jpwahle/emnlp24-prompt-paraphrase
https://github.com/jpwahle/prompt-paraphrase
https://github.com/jsbaan/DPAC-DialogueGAN
https://github.com/jsnouffer/transfer-learning-conv-ai
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/jugalw13/Red-Hat-Hack
https://github.com/juletx/image-caption-generation
https://github.com/juliakreutzer/joeynmt
https://github.com/julianser/hed-dlg-truncated
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
https://github.com/jungokasai/t2r
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/junhahyung/bert_finetune
https://github.com/junhahyung/bert_transfer
https://github.com/junshutang/Make-It-3D
https://github.com/justinchiu/NeuralDialog
https://github.com/juyongjiang/codeup
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
https://github.com/jxmorris12/unsupervised-text-deidentification
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order
https://github.com/k-randl/self-explaining_llms
https://github.com/kaituoxu/Speech-Transformer
https://github.com/kaituoxu/SpeechTransformer
https://github.com/kaletap/language-style-transfer-pytorch
https://github.com/kaletap/nlp-experiments
A Probabilistic Formulation of Unsupervised Text Style Transfer
https://github.com/kaletap/nlp-style-transfer
https://github.com/karenacorn99/explore-bert
https://github.com/karpathy/char-rnn
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.