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Found 7 papers, 2 papers with code
Embodied LLM Agents Learn to Cooperate in Organized Teams
Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-related tasks.
Training Language Model Agents without Modifying Language Models
Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions.
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints
Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms.
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
However, since the prompts need to be sampled from a large volume of annotated examples, finding the right prompt may result in high annotation costs.
Federated Class-Incremental Learning with Prompting
For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid).
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks.
Prototype-Based Layered Federated Cross-Modal Hashing
And due to the problems of statistical heterogeneity, model heterogeneity, and forcing each client to accept the same parameters, applying federated learning to cross-modal hash learning becomes very tricky.
