https://github.com/vietai/vit5
공식 코드ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation
Papers with Code 아카이브
Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/vijini/generatedtextdetection
공식 코드A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications
https://github.com/vinairesearch/lampat
공식 코드LAMPAT: Low-Rank Adaption for Multilingual Paraphrasing Using Adversarial Training
https://github.com/vineetjohn/linguistic-style-transfer
공식 코드Disentangled Representation Learning for Non-Parallel Text Style Transfer
https://github.com/vinh-ai/a3net
공식 코드Anatomical Attention Alignment representation for Radiology Report Generation
https://github.com/vinusankars/reliability-of-ai-text-detectors
공식 코드https://github.com/vipulraheja/coedit
공식 코드https://github.com/virobo-15/mgcc
공식 코드https://github.com/vityavitalich/imad
공식 코드https://github.com/vivian556123/neurips2024-covomix
공식 코드CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations
https://github.com/vklabmipt/implicit-unlikelihood-training
공식 코드Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning
https://github.com/voidful/BDG
공식 코드A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies
https://github.com/voidism/L2KD
공식 코드https://github.com/vsrana-ai/IC-AnnoMI
공식 코드Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health
https://github.com/vyraun/finding-memo
공식 코드Finding Memo: Extractive Memorization in Constrained Sequence Generation Tasks
https://github.com/vyraun/literalness
공식 코드https://github.com/vyraun/long-tailed
공식 코드https://github.com/waldenruc/ufo
공식 코드UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models
https://github.com/wangchunliu/drs-pretrained-lmm
공식 코드Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation
https://github.com/wangclnlp/csem
공식 코드Learning Evaluation Models from Large Language Models for Sequence Generation
https://github.com/wangcongcong123/ttt
공식 코드https://github.com/wanghm92/rw_fg
공식 코드Revisiting Challenges in Data-to-Text Generation with Fact Grounding
https://github.com/wangjs9/aligned-dpm
공식 코드Aligning Language Models with Human Preferences via a Bayesian Approach
https://github.com/wangpf3/imagine-and-verbalize
공식 코드Contextualized Scene Imagination for Generative Commonsense Reasoning
https://github.com/wangyu-ustc/memoryllm
공식 코드https://github.com/wangyuchi369/ladic
공식 코드https://github.com/wasiahmad/PLBART
공식 코드Unified Pre-training for Program Understanding and Generation
https://github.com/wcshin-git/te-vqgan
공식 코드https://github.com/webis-de/ACL-21
공식 코드Argument Undermining: Counter-Argument Generation by Attacking Weak Premises
https://github.com/webis-de/eacl21-belief-based-claim-generation
공식 코드https://github.com/webis-de/scolia25-xsum
공식 코드Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
https://github.com/weichen-yu/lm-extraction
공식 코드Bag of Tricks for Training Data Extraction from Language Models
https://github.com/weikang-wang/NationalityBiasOfChatGPT
공식 코드Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPT
https://github.com/weixiangyan/clinicallab
공식 코드ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World
https://github.com/wellecks/nonmonotonic_text
공식 코드https://github.com/wengsyx/lingyi
공식 코드LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.