Large Language Models (LLMs) have revolutionized open-domain dialogue agents but
encounter challenges in multi-character role-playing (MCRP) scenarios. To address the …

The approach of Reinforcement Learning from Human Feedback (RLHF) is widely used for
enhancing pre-trained Language Models (LM), enabling them to better align with human …

Role-playing has wide-ranging applications in customer support, embodied agents,
computational social science, etc. The influence of parametric world knowledge of large …

Recent advancements in large language models (LLMs) have significantly boosted the rise
of Role-Playing Language Agents (RPLAs), ie, specialized AI systems designed to simulate …

Large language model (LLM) based agents have recently attracted much attention from the
research and industry communities. Compared with original LLMs, LLM-based agents are …

Embodied AI agents responsible for executing interconnected, long-sequence household
tasks often face difficulties with in-context memory, leading to inefficiencies and errors in task …

Existing datasets for narrative understanding often fail to represent the complexity and
uncertainty of relationships in real-life social scenarios. To address this gap, we introduce a …

In this demo, we present NarrativePlay--an innovative system enabling users to role-play a
fictional character and interact with dynamically generated narrative environments. Unlike …

We introduce SimulBench, a benchmark designed to evaluate large language models
(LLMs) across a diverse collection of creative simulation scenarios, such as acting as a …

Large language models (LLMs) have demonstrated impressive performance and spurred
numerous AI applications, in which role-playing agents (RPAs) are particularly popular …