Papers with Code 아카이브
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/farrell236/RATCHET
공식 코드RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting
https://github.com/fasterdecoding/rest
공식 코드https://github.com/fastnlp/cpt
공식 코드CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
https://github.com/fastnlp/nlp-dataset
공식 코드Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
https://github.com/fastnlp/style-transformer
공식 코드Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
https://github.com/fawazsammani/nlxgpt
공식 코드Uni-NLX: Unifying Textual Explanations for Vision and Vision-Language Tasks
https://github.com/fawazsammani/uni-nlx
공식 코드Uni-NLX: Unifying Textual Explanations for Vision and Vision-Language Tasks
https://github.com/fe1ixxu/vt-stower
공식 코드VAE based Text Style Transfer with Pivot Words Enhancement Learning
https://github.com/feielysia/viecap
공식 코드Transferable Decoding with Visual Entities for Zero-Shot Image Captioning
https://github.com/felix-last/kmeans_smote
공식 코드Oversampling for Imbalanced Learning Based on K-Means and SMOTE
https://github.com/fenglinliu98/MIA
공식 코드Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
https://github.com/fengranmark/convsdg
공식 코드https://github.com/feyzaakyurek/bias-textgen
공식 코드Challenges in Measuring Bias via Open-Ended Language Generation
https://github.com/fhaer/multi-agent-llm-system
공식 코드Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications
https://github.com/fjq-tongji/mllm-sul
공식 코드https://github.com/fjzzq2002/random_transformers
공식 코드https://github.com/fladhak/contrastive_error_attribution
공식 코드https://github.com/flagopen/flagembedding
공식 코드Making Large Language Models A Better Foundation For Dense Retrieval
https://github.com/flair-iisc/richer-countries-have-richer-output
공식 코드https://github.com/flame-chasers/syntbpr
공식 코드An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval
https://github.com/flbbb/locost-summarization
공식 코드LOCOST: State-Space Models for Long Document Abstractive Summarization
https://github.com/florianbaud/retrieval-augmented-mds
공식 코드Non-Parametric Memory Guidance for Multi-Document Summarization
https://github.com/florianmai/emb2emb
공식 코드https://github.com/florin-git/Base-vs-Instruct-LLMs-in-RAG-Systems
공식 코드A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems
https://github.com/florin-git/The-Power-of-Noise
공식 코드https://github.com/for-ai/goodtriever
공식 코드Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models
https://github.com/forever208/ddpm-ip
공식 코드Input Perturbation Reduces Exposure Bias in Diffusion Models
https://github.com/francis-hsu/quantgenmdl
공식 코드Generative quantum machine learning via denoising diffusion probabilistic models
https://github.com/francois-meyer/ssmt
공식 코드Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation
https://github.com/francois-meyer/sspg
공식 코드https://github.com/francois-meyer/t2x
공식 코드https://github.com/freesunshine0316/neural-graph-to-seq-mp
공식 코드https://github.com/frxxzhl/ssebt
공식 코드https://github.com/fsoft-ailab/poem-generator
공식 코드SP-GPT2: Semantics Improvement in Vietnamese Poetry Generation
https://github.com/fusiming3/mars
공식 코드MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.