Large Language Model
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos Zechen Bai 1 Tong He2 Haiyang Mei 1 Pichao Wang 2
We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoning capabilities and world knowledge of large language models, and augmented by the Segment Anything Model, VideoLISA generates temporally consistent segmentation masks in videos based on language instructions.
Linking In-context Learning in Transformers to Human Episodic Memory Li Ji-An
Understanding connections between artificial and biological intelligent systems can reveal fundamental principles of general intelligence.
KnowGPT: Knowledge Graph based PrompT ing for Large Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications.
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
GNN's representations into a fixed number of graph token embeddings without
Can Graph Learning Improve Planning in LLM-based Agents?
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs).
AGILE: A Novel Reinforcement Learning Framework of LLM Agents
The agent possesses capabilities beyond conversation, including reflection, tool usage, and expert consultation.