https://github.com/huashen218/multiturncleanup
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
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.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Autoregressive Knowledge Distillation through Imitation Learning
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Unifying Human and Statistical Evaluation for Natural Language Generation
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
Neural Machine Translation by Jointly Learning to Align and Translate
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis
Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog
Personalizing Dialogue Agents: I have a dog, do you have pets too?
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
Wizard of Wikipedia: Knowledge-Powered Conversational agents
Neural Machine Translation by Jointly Learning to Align and Translate
A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.