Zini Yang

In this project, we observed that most existing evaluation methods rely solely on explicitly defined criteria, which limits their ability to meaningfully assess LLM creativity. To address this limitation, we proposed a method that incorporates human responses to better evaluate the LLM creativity. Our approach consists of two steps: 1) we create a boundary in the embedding space by identifying the smallest convex hull that contains human responses above a density threshold; 2) we introduce the LLM coverage rate, which measures the proportion of LLM outputs that fall within this human-response-grounded boundary. Across a range of tasks, including divergent thinking, code generation, and creative writing, our method exhibited strong generalizability: the LLM coverage rate consistently decreased as tasks required more detailed and thoughtful responses. This demonstrates that our human-response-grounded framework provides a better way to evaluate LLM creativity.

The project focuses on early detection of vaccine-related risks. Because news coverage tends to spike only after crises escalate, early warning signals buried in open-source data remain underutilized. To address this challenge, I developed an end-to-end pipeline that processes terabytes of Common Crawl data. To manage the computational scale, I implemented a FastText-based coarse filter, achieving ~60% precision while running over 1,000× faster than LLM-based classification. I then applied a locally hosted Qwen model for fine-grained labeling using carefully validated prompts and annotations. To enhance interpretability, I integrated UMAP-based clustering and LLM-generated summaries to reveal emerging discourse patterns.

This research addresses the asymmetry where data defenders possess less advanced technology than professional scrapers. We formulate the protection problem as a dynamic game where protectors choose defense levels periodically and attackers optimize attack timing. A Markov model over breach history analyzes evolving deterrence dynamics and derives optimal strategies that maximize long-term defense efficiency.

Conducted an empirical analysis of how individuals form expectations about a certain variable using information from multiple, possibly correlated sources. Examined deviations from rational benchmarks when signals overlap.

Analyzed how people form and retain costly, mutually-agreed social connections to cooperate on providing social goods. Studied stability of networks and how incentive design influences cooperative ties.