https://github.com/nlpaueb/dmmcs
공식 코드A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning
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.
A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning
Parallel Refinements for Lexically Constrained Text Generation with BART
Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet
Adaptive and Personalized Exercise Generation for Online Language Learning
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
RouteExplainer: An Explanation Framework for Vehicle Routing Problem
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
Preference-Guided Reflective Sampling for Aligning Language Models
Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization
A Sentiment Consolidation Framework for Meta-Review Generation
PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
TouchStone: Evaluating Vision-Language Models by Language Models
MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer
Lost-in-the-Middle in Long-Text Generation: Synthetic Dataset, Evaluation Framework, and Mitigation
Learning Transferable Visual Models From Natural Language Supervision
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler
The All-Seeing Project V2: Towards General Relation Comprehension of the Open World
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.