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/eval4nlp/sharedtask2023
공식 코드The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics
https://github.com/event-ahu/medical_image_analysis
공식 코드R2GenCSR: Retrieving Context Samples for Large Language Model based X-ray Medical Report Generation
https://github.com/ewanwong/rsa-control
공식 코드RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework
https://github.com/ewsheng/controllable-nlg-biases
공식 코드https://github.com/ewsheng/decoding-biases
공식 코드Societal Biases in Language Generation: Progress and Challenges
https://github.com/ewsheng/nlg-bias
공식 코드The Woman Worked as a Babysitter: On Biases in Language Generation
https://github.com/explainableml/clevr-x
공식 코드CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
https://github.com/explanare/char-iit
공식 코드https://github.com/eyal-orbach/Facts2Story-data
공식 코드https://github.com/fabrahman/Emo-Aware-Storytelling
공식 코드Modeling Protagonist Emotions for Emotion-Aware Storytelling
https://github.com/fabrahman/rebart
공식 코드https://github.com/facebook/MemNN
공식 코드https://github.com/facebookresearch/EmpatheticDialogues
공식 코드Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
https://github.com/facebookresearch/ParlAI
공식 코드Personalizing Dialogue Agents: I have a dog, do you have pets too?
https://github.com/facebookresearch/docagent
공식 코드DocAgent: A Multi-Agent System for Automated Code Documentation Generation
https://github.com/facebookresearch/editeval
공식 코드EditEval: An Instruction-Based Benchmark for Text Improvements
https://github.com/facebookresearch/electra-fewshot-learning
공식 코드Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models
https://github.com/facebookresearch/fairseq
공식 코드https://github.com/facebookresearch/momi
공식 코드Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems
https://github.com/facebookresearch/responsiblenlp
공식 코드https://github.com/facebookresearch/ternary_binary_transformer
공식 코드https://github.com/facebookresearch/unlikelihood_training
공식 코드https://github.com/facebookresearch/vizseq
공식 코드https://github.com/facico/goat-peft
공식 코드https://github.com/fadedcosine/dependency-guided-neural-text-generation
공식 코드https://github.com/fadedcosine/pos-guided-neural-text-generation
공식 코드Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling
https://github.com/faisaltareque/XL-HeadTags
공식 코드https://github.com/fajri91/RSTExtractor
공식 코드https://github.com/fallingnight/pspg
공식 코드https://github.com/fangleai/Implicit-LVM
공식 코드https://github.com/fangleai/Outline2Story
공식 코드Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events
https://github.com/fangleai/TransformerCVAE
공식 코드Transformer-based Conditional Variational Autoencoder for Controllable Story Generation
https://github.com/fangleigit/RetroSub
공식 코드Leveraging Reaction-aware Substructures for Retrosynthesis Analysis
https://github.com/fanqiwan/fusellm
공식 코드https://github.com/fantast4ever/knowprefix-tuning
공식 코드KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.