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Found 5 papers, 2 papers with code
SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents
With the integration of large language models (LLMs), embodied agents have strong capabilities to understand and plan complicated natural language instructions.
Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review
However, the unchecked adoption of LLMs poses significant risks to the integrity of the peer review system.
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
Post-training is essential for enabling large language models (LLMs) to follow human instructions.
Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
Drawing from the sociological insight that acknowledging all parties' concerns is a key factor in shaping human values, this paper proposes a novel direction to align LLMs by themselves: social scene simulation.
Pragmatic Communication in Multi-Agent Collaborative Perception
Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration.
