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1 day ago · Storytelling is an integral part of human experience and plays a crucial role in social interactions. Thus, Automatic Story Evaluation (ASE) and Generation (ASG) could benefit society in multiple ways, but they are challenging tasks which require high-level human abilities such as creativity, reasoning and deep understanding. Meanwhile, Large Language Models (LLM) now achieve state-of-the-art ...
3 days ago · Video generation research and development is strongly advancing and expanding with new entrants. Powerful new text-to-video (T2V) models have emerged in recent months following models earliest to ...
5 days ago · 3) Interactive Story Generation with Reinforcement Learning: Develop an interactive storytelling system using reinforcement learning. Users can guide the story’s direction, and the model adapts its narrative generation based on user interactions, creating a dynamic and personalized storytelling experience.
- What are some popular types of Generative AI projects?Popular types include image synthesis, text generation, music composition, code generation, and various creative applications like art and design p...
- How can I get started with a Generative AI project?Start by understanding Generative AI concepts and choose a specific domain or type of content you want to generate. Explore frameworks and models r...
- What programming languages are commonly used for Generative AI projects?Python is widely used for Generative AI projects. Frameworks like TensorFlow and PyTorch, along with libraries like OpenCV and NLTK, are commonly e...
- Are there pre-trained models available for Generative AI projects?Yes, many pre-trained models are available, such as GPT for text generation, DALL-E for image synthesis, and various models for music generation. T...
- Can Generative AI be used for practical applications beyond creative projects?Absolutely. Generative AI has practical applications in areas like medical image generation, code completion, language translation, and even in sim...
1 day ago · To this end, we construct a new model framework for question generation. Under this framework, first, we extracted answers incorporating narrative elements based on stories using named entity recognition and syntactic semantic dependencies. Next, story passages were refined and summarised.
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3 days ago · The model is tailored to the unique enterprise-specific datasets. These can range from product descriptions and customer feedback to internal communications like Slack messages.
3 days ago · Generation Phase: The AI processes the prompt and generates a coherent narrative. It uses its understanding of language and context to create engaging and plausible storylines, often in real-time. Refinement Phase: Users can iteratively refine the output by providing feedback or additional prompts, guiding the AI to produce the desired narrative.
