https://github.com/alibaba/AliceMind/tree/main/PALM
공식 코드PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned 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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition
Unifying Structured Data as Graph for Data-to-Text Pre-Training
ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Tailor: Generating and Perturbing Text with Semantic Controls
Automatic Logical Forms improve fidelity in Table-to-Text generation
Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity
CoCon: A Self-Supervised Approach for Controlled Text Generation
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
CERET: Cost-Effective Extrinsic Refinement for Text Generation
Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding
CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation
DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.