most citedToo Good to be Bad: On the Failure of LLMs to Role-Play Villains

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20252 cited

Too Good to be Bad: On the Failure of LLMs to Role-Play Villains

Zihao Yi, Qingxuan Jiang, Ruotian Ma +8

Large Language Models (LLMs) are increasingly tasked with creative generation, including the simulation of fictional characters. However, their ability to portray non-prosocial, an…

cs.CL2025

Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare

Zhengliang Shi, Ruotian Ma, Jen-tse Huang +14

Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. However, the principles and values that guide these models when distri…

cs.CL2025

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs

Yue Wang, Ruotian Ma, Xingyu Chen +12

The rise of Large Language Models (LLMs) is reshaping multimodel models, with speech synthesis being a prominent application. However, existing approaches often underutilize the li…

cs.CL20251 cited

The Hunger Game Debate: On the Emergence of Over-Competition in Multi-Agent Systems

Xinbei Ma, Ruotian Ma, Xingyu Chen +14

LLM-based multi-agent systems demonstrate great potential for tackling complex problems, but how competition shapes their behavior remains underexplored. This paper investigates th…

cs.AI2025

Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training

Mengru Wang, Xingyu Chen, Yue Wang +12

Mixture-of-Experts (MoE) architectures within Large Reasoning Models (LRMs) have achieved impressive reasoning capabilities by selectively activating experts to facilitate structur…