nlp16 GAN: Generative Adversarial Networks Generative Adversarial Nets Ian J. Goodfellow, Jean Pouget-Abadie, Yoshua Bengio, etc. 10 Jun 2014 Deep Generative Models The ultimate goal of generative model is to approximate the ground-truth distribution of the data by using a neural network, and generate a data by sampling from the distribution. Surprisingly, generative models weren't that useful until among 2015, but it have recently impro.. 2023. 3. 13. Detecting Whether A Text is Written in GPT DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature Eric Mitchell, Yoonho Lee, Chelsea Finn, etc. 26 Jan 2023 DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature The fluency and factual knowledge of large language models (LLMs) heightens the need for corresponding systems to detect whether a piece of text is machine-written. For example.. 2023. 2. 27. Detour for Domain-Specific Tasks Avoiding Adaptive Pre-training KALA: Knowledge-Augmented Language Model Adaptation Minki Kang, Jinheon Baek, Sung Ju Hwang 4 Aug 2022 Abstract 2023. 2. 7. LUKE: Language Understanding with Knowledge-Based Embeddings LUKE: Deep Contextualized Entity Representation with Entity-aware Self-attention Ikuya Yamade, Akari Asai, etc. 2 Oct 2020 Abstract As the title exposes, this paper, which indicates LUKE, proposes a deeply contextualized entity representations based on bidirectional transformer. Luke has achieved state-of-the-art on five well-known entity related tasks, such as NER (Named Entity Recognition) and.. 2023. 2. 4. 이전 1 2 3 4 다음
