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/flxst/nerblackbox
https://github.com/flyliu2017/bert
https://github.com/flyliu2017/bert_modularized
https://github.com/for-ai/llm-profiling-toolkit
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
https://github.com/formiel/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/fqyshelly/CS224n-Winter2019-assignment-solutions
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/fra-mari/The-Karl-Marx-Press-Review
https://github.com/frankcgq105/BERTCHEN
https://github.com/franknb/Self-attention-DCGAN
https://github.com/franknb/Text-Summarization
https://github.com/freedomintelligence/chatgpt-detection-pr-hppt
Is ChatGPT Involved in Texts? Measure the Polish Ratio to Detect ChatGPT-Generated Text
https://github.com/freesunshine0316/RaST-plus
https://github.com/freshllms/freshqa
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
https://github.com/frozentoad9/Neural-Machine-Translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/fstahlberg/tensor2tensor
https://github.com/funaudiollm/inspiremusic
https://github.com/fuqiang-git-hub/unilmv1-Paddle
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/fuqianya/ViLBERT-Paddle
https://github.com/fuzhenxin/text_style_transfer
https://github.com/fwbrandao/Abstractive_Summarisation
https://github.com/g4vrel/CFM
https://github.com/ga642381/SpeechPrompt
https://github.com/galsang/galsang
Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
https://github.com/gavincaoyuji/NLP
https://github.com/gfoxx29/Image-Captioning
https://github.com/gggggeun/DeepLearning_Architecture
https://github.com/giagia96/DeepComedy
https://github.com/giannispana/ails-ntua-at-semeval-2024-task-9-brainteaser
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
https://github.com/giantseaweed/decree
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/gicheonkang/prograsp
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
https://github.com/glaciohound/lm-steer
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmftbyGMFTBY/HashRetrieval
Group-wise Contrastive Learning for Neural Dialogue Generation
https://github.com/gmum/dl-mo-2021
https://github.com/going-doer/paper2code
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.