https://github.com/ruc-nlpir/flashrag
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
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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.
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
A Hybrid Convolutional Variational Autoencoder for Text Generation
Learning Transferable Visual Models From Natural Language Supervision
Studying the role of named entities for content preservation in text style transfer
Controlled Text Generation as Continuous Optimization with Multiple Constraints
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Effective Approaches to Attention-based Neural Machine Translation
Learning Transferable Visual Models From Natural Language Supervision
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Learning Transferable Visual Models From Natural Language Supervision
Structured Denoising Diffusion Models in Discrete State-Spaces
Neural Machine Translation by Jointly Learning to Align and Translate
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Effective Approaches to Attention-based Neural Machine Translation
Learning Transferable Visual Models From Natural Language Supervision
Effective Approaches to Attention-based Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.