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HumanLLM: Towards Personalized Understanding and Simulation of Human Nature
Yuxuan Lei, Tianfu Wang, Jianxun Lian +3
Motivated by the remarkable progress of large language models (LLMs) in objective tasks like mathematics and coding, there is growing interest in their potential to simulate human…
Population-Aligned Persona Generation for LLM-based Social Simulation
Zhengyu Hu, Jianxun Lian, Zheyuan Xiao +10
Recent advances in large language models (LLMs) have enabled large-scale, high-fidelity social simulations, creating new opportunities for computational social science. However, co…
MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models?
Xixian Yong, Jianxun Lian, Xiaoyuan Yi +2
Large language models (LLMs) have been widely adopted as the core of agent frameworks in various scenarios, such as social simulations and AI companions. However, the extent to whi…
Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
Zhengyu Hu, Jianxun Lian, Zheyuan Xiao +5
Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning, yet their learning ability, which is crucial for adapting t…
CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds
Lei Wang, Jianxun Lian, Yi Huang +5
Role-playing is a crucial capability of Large Language Models (LLMs), enabling a wide range of practical applications, including intelligent non-player characters, digital twins, a…
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
Cheng Li, Jindong Wang, Yixuan Zhang +6
Emotional intelligence significantly impacts our daily behaviors and interactions. Although Large Language Models (LLMs) are increasingly viewed as a stride toward artificial gener…
Towards Better Entity Linking with Multi-View Enhanced Distillation
Yi Liu, Yuan Tian, Jianxun Lian +7
Dense retrieval is widely used for entity linking to retrieve entities from large-scale knowledge bases. Mainstream techniques are based on a dual-encoder framework, which encodes…
A Mutually Reinforced Framework for Pretrained Sentence Embeddings
Junhan Yang, Zheng Liu, Shitao Xiao +5
The lack of labeled data is a major obstacle to learning high-quality sentence embeddings. Recently, self-supervised contrastive learning (SCL) is regarded as a promising way to ad…