https://github.com/lwgkzl/MedDG
공식 코드MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
Papers with Code 아카이브
Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 155
- 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/lwgkzl/multiesc
공식 코드Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
https://github.com/lxchtan/omnidrca
공식 코드https://github.com/lxchtan/tegtok
공식 코드TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
https://github.com/lyy1994/plm_as_kb
공식 코드Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation
https://github.com/lzy-the-boys/miracle
공식 코드https://github.com/m-e-agi-lab/muddit
공식 코드Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model
https://github.com/m3hrdadfi/mogpt
공식 코드An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems
https://github.com/machamp-nlp/machamp
공식 코드Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
https://github.com/machelreid/editpro
공식 코드https://github.com/machelreid/lewis
공식 코드LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
https://github.com/machine-teaching-group/lak2024_gpt4-hints-gpt3.5val
공식 코드https://github.com/machine-teaching-group/lak2024_gpt4hints-gpt3.5val
공식 코드https://github.com/mailong25/peft_diversity
공식 코드Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning
https://github.com/mailong25/rts2s-dg
공식 코드https://github.com/mainlp/donkii
공식 코드Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?
https://github.com/mainlp/makieval
공식 코드https://github.com/mainlp/mcq-mismatch
공식 코드https://github.com/majiangqin/Image-to-Recipe-Translation-using-AI
공식 코드https://github.com/majid-daliri/disd
공식 코드Coupling without Communication and Drafter-Invariant Speculative Decoding
https://github.com/majumderb/recipe-personalization
공식 코드Generating Personalized Recipes from Historical User Preferences
https://github.com/malllabiisc/SGCP
공식 코드https://github.com/maloyan/ruatd
공식 코드https://github.com/manestay/careful-with-context
공식 코드$\rm{C {\small IS}}^2$: A Simplified Commonsense Inference Evaluation for Story Prose
https://github.com/manestay/cis2
공식 코드$\rm{C {\small IS}}^2$: A Simplified Commonsense Inference Evaluation for Story Prose
https://github.com/manga-uofa/eqhard-em
공식 코드An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation
https://github.com/manga-uofa/lmreward
공식 코드Teacher Forcing Recovers Reward Functions for Text Generation
https://github.com/manga-uofa/nacc
공식 코드A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
https://github.com/manga-uofa/prompt-edit
공식 코드https://github.com/mankeerat/orsum-meta-review-generation
공식 코드Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation
https://github.com/marco-roberti/char-data-to-text-gen
공식 코드Copy mechanism and tailored training for character-based data-to-text generation
https://github.com/marcoguerini/CONAN
공식 코드Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering
https://github.com/marian-nmt/marian
공식 코드The University of Edinburgh's Submission to the WMT22 Code-Mixing Shared Task (MixMT)
https://github.com/markchenyutian/t5-sentinel-public
공식 코드Token Prediction as Implicit Classification to Identify LLM-Generated Text
https://github.com/martiansideofthemoon/ai-detection-paraphrases
공식 코드Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
https://github.com/martiansideofthemoon/hurdles-longform-qa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.