I am Yingjia Wan, a first year PhD student at UCLA, advised by Prof. Elisa Kreiss. My research interests include (but are not limited to) LLM reasoning, evaluation, AI4MATH, and post-training alignment.
- Education: I obtained my master’s degree at the Language Technology lab, University of Cambridge in 2023. I received my bachelor’s degree from University of Macau where I graduated as the valedictorian of Class 2021.
- Collaboration: Working on AI/NLP since 2023, I feel incredibly grateful to have learned from some outstanding AI researchers and mentors such as: Prof. Elisa Kreiss, Prof. Zhijiang Guo, Dr. Ivan Vulić, Dr. Jianqiao Lu, Dr. Yinya Huang, and Dr. Zhengying Liu.
- Status: I am always open for a chat or collaboration. Feel free to drop me an email!
University of California, Los Angeles (PhD), 2025-current
University of Cambridge (MPhill), 2022-2023
University of Macau (BA), 2017-2021

FaStfact is a multi-agent pipeline for evaluating long-form generation factuality that achieves the highest alignment with human evaluation and time/token efficiency among existing baselines. An annotated FaStfact-bench is also open-sourced.

SATBench is a benchmark for evaluating LLMs logical reasoning through logical puzzles derived from Boolean satisfiability (SAT) problems.

FormalAlign aims to address a long-lasting challenge in autoformalization - the absence of an effective alignment evaluation metric. It accurately assesses the semantic and logical alignment between the natural language and formal theorems.

MR-BEN is a comprehensive process-based benchmark to evaluate advanced `meta-reasoning’ skills, where models are asked to locate and analyse errors in the provided CoT solutions. It comprises 5,975 multi-domain samples with annotated groundtruths.