https://github.com/allenai/FineGrainedRLHF
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Meta-Context Transformers for Domain-Specific Response Generation
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Effective Approaches to Attention-based Neural Machine Translation
HuggingFace's Transformers: State-of-the-art Natural Language Processing
HuggingFace's Transformers: State-of-the-art Natural Language Processing
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
How to Design Translation Prompts for ChatGPT: An Empirical Study
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Table-to-text Generation by Structure-aware Seq2seq Learning
Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue
LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.