Target Concept

Target concept research focuses on leveraging machine learning, particularly large language models (LLMs) and diffusion models, to generate, manipulate, and understand concepts represented in various data modalities, including text, images, and 3D meshes. Current research emphasizes improving the accuracy and efficiency of these processes, addressing challenges like concept inhibition in generative models and developing methods for cross-target knowledge transfer and concept ablation. This work has significant implications for diverse fields, including drug discovery, image generation, and knowledge graph construction, by enabling more efficient and effective data analysis and synthesis.