activity
20212026
most citedExpeL: LLM Agents Are Experiential Learners

16 citations · 29 across the 25 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

cs.CL2024

Optimizing Social Media Annotation of HPV Vaccine Skepticism and Misinformation Using Large Language Models: An Experimental Evaluation of In-Context Learning and Fine-Tuning Stance Detection Across Multiple Models

Luhang Sun, Varsha Pendyala, Yun-Shiuan Chuang +6

This paper leverages large-language models (LLMs) to experimentally determine optimal strategies for scaling up social media content annotation for stance detection on HPV vaccine-…

cs.CL2024★ 1 cited

Scaffolded Language Models with Language Supervision for Mixed-Autonomy: A Survey

Matthieu Lin, Jenny Sheng, Andrew Zhao +7

This survey organizes the intricate literature on the design and optimization of emerging structures around post-trained LMs. We refer to this overarching structure as scaffolded L…

cs.AI2024★ 1 cited

Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing

Huanqian Wang, Yang Yue, Rui Lu +5

Large Language Models (LLMs) have demonstrated great potential as generalist assistants, showcasing powerful task understanding and problem-solving capabilities. To deploy LLMs as…

cs.LG2024

Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories

Yiqiao Jin, Andrew Zhao, Yeon-Chang Lee +3

We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent adv…

cs.LG2024

DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints

Andrew Zhao, Quentin Xu, Matthieu Lin +4

Recent advances in large language model assistants have made them indispensable, raising significant concerns over managing their safety. Automated red teaming offers a promising a…

cs.LG2024

Exploring Text-to-Motion Generation with Human Preference

Jenny Sheng, Matthieu Lin, Andrew Zhao +5

This paper presents an exploration of preference learning in text-to-motion generation. We find that current improvements in text-to-motion generation still rely on datasets requir…