My research group:

Overarching Research Themes

*Extracted by GPT-4, there may be inconsistencies.* #### *Ethics in AI Development* My research group explores the complex dimensions of ethical AI and its implications for society. One of our pivotal works, [HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions](http://arxiv.org/abs/2409.16427), introduces a framework designed to simulate potential safety risks associated with human-AI interactions. We also investigate the balance between usability and ethical considerations through our paper, [AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents](https://arxiv.org/abs/2409.09013), which highlights the dilemmas faced by LLMs in delivering truthful responses. Our efforts culminate in [Particip-AI: A Democratic Surveying Framework](https://arxiv.org/abs/2403.14791) that aims to engage a broader audience in discussions of future AI applications and their impacts. #### *Narrative Dynamics in AI* My research group explores the way narratives are constructed and perceived through AI technologies. We delve into emotional resonance in storytelling with our important study, [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://arxiv.org/abs/2405.17633), which examines how narrative styles can evoke empathy. Additionally, our work, [Modeling Empathic Similarity in Personal Narratives](https://arxiv.org/abs/2305.14246), quantifies connections between audiences and narratives, enriching our understanding of storytelling mechanics. Another significant contribution, [Quantifying the narrative flow of imagined versus autobiographical stories](https://www.pnas.org/doi/10.1073/pnas.2211715119), provides insights into how different types of personal stories affect listener engagement. #### *Social Intelligence and Simulation* My research group explores how social intelligence can be embedded and evaluated in language models. We critically assess the limitations of simulating genuine interpersonal engagements in our study, [Is This the Real Life? Is This Just Fantasy? The Misleading Success of Simulating Social Interactions With LLMs](http://arxiv.org/abs/2403.05020), which reveals the gaps between simulated responses and authentic human communication. To foster more effective models, we research the efficacy of context in understanding social dynamics through [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://arxiv.org/abs/2305.14763). Our ongoing efforts also led to [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://arxiv.org/abs/2310.11667), which advances the measures of social reasoning capabilities among AI agents. #### *Addressing Toxic Language* My research group explores the pressing challenges in mitigating toxic language generation within AI systems. We highlight multilingual aspects of this issue with our notable study, [PolygloToxicityPrompts: Multilingual Evaluation of Neural Toxic Degeneration in Large Language Models](https://arxiv.org/abs/2405.09373), which scrutinizes how toxic content manifests across languages. Another critical observation is presented in [Counterspeakers’ Perspectives: Unveiling Barriers and AI Needs in the Fight against Online Hate](https://arxiv.org/abs/2403.00179), offering insights into the requirements for combating hate speech effectively. Additionally, our work, [Leftover-Lunch: Advantage-based Offline Reinforcement Learning for Language Models](https://arxiv.org/abs/2305.14718), investigates reinforcement learning strategies to enhance ethical dialogue generation.