About

Hi! I'm Ziche Liu, a first-year Ph.D. student in Computer Science and Engineering at UC San Diego, where I am fortunate to be advised by Prof. Jingbo Shang. I received my B.S. from the Chinese University of Hong Kong, Shenzhen, and spent a year at UC Berkeley as a visiting student during my undergraduate studies.

Previously, I worked closely with Dr. Feng Jiang under the supervision of Prof. Haizhou Li on data selection for LLM fine-tuning, and with Prof. Benyou Wang on model localization and bias analysis in LLM-as-a-judge. During my time at UC Berkeley, I also worked on world models at Berkeley AI Research (BAIR).

My research interests broadly lie in LLM, agents, and embodied world models, particularly in understanding, evaluating, and improving their ability to learn, reason, and interact with complex environments. I'm always happy to connect and chat about research so please feel free to reach out!

Research

I work at the intersection of NLP/LLMs and embodied world models, asking how data, architectures, and evaluation shape what models really learn.

Language & LLMs. My NLP work has studied which data actually helps large models, how human and LLM judges can be biased, and how to adapt systems across cultures. I think of language as a compact interface on top of richer perception and interaction, not a full replacement for them.

Embodied world models. At BAIR, I build action-conditioned video world models that let agents imagine futures and respond to control signals over long horizons. More broadly, I'm interested in models that learn language, vision, and action together, instead of treating one as a small add-on to the others.

Evaluation & learning over time. I also work on how to evaluate these grounded agents in interactive, changing settings, where they need to handle uncertainty, ask good questions, and keep learning without forgetting.

Broader curiosities. I'm intrigued by human memory, consciousness, and intelligence, and how they might quietly inspire the next generation of grounded models.

Publications

BOOKMARKS: Efficient Active Storyline Memory for Role-playing

BOOKMARKS: Efficient Active Storyline Memory for Role-playing

Letian Peng, Ziche Liu, Yiming Huang, Longfei Yun, Kun Zhou, Yupeng Hou, Jingbo Shang

preprint

Paper/ Code

TL;DR: We introduce BOOKMARKS, an efficient active storyline memory system that selectively maintains evolving narrative states for long-horizon role-playing.

Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models

Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models

Ziche Liu*, Rui Ke*, Feng Jiang, Haizhou Li (*=Equal Contribution)

NAACL 2025

Paper/ Code/ Demo/ Talk

TL;DR: We propose a three-stage scheme to standardize data selection methods and develop two metrics (efficiency and flexibility) to evaluate the effectiveness of a data selector.

Humans or LLMs as the Judge? A Study on Judgement Biases

Humans or LLMs as the Judge? A Study on Judgement Biases

Guiming Chen*, Shunian Chen*, Ziche Liu, Feng Jiang, Benyou Wang (*=Equal Contribution)

EMNLP 2024

Paper/ Data

TL;DR: We investigate judgment biases in human and LLM judges, demonstrating their vulnerability through experiments with a curated dataset based on Bloom's Taxonomy.

AceGPT, Localizing Large Language Models in Arabic

AceGPT, Localizing Large Language Models in Arabic

Huang Huang*, Fei Yu*, Jianqing Zhu*, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu (*=Equal Contribution)

NAACL 2024

Paper/ Code/ Demo

TL;DR: We leverage high-quality SFT data and culturally-aware RLAIF to effectively localize LLM for Arabic, addressing cultural sensitivity and local values.

Education

UC San Diego
2026.09 — now
Ph.D. Student in Computer Science and Engineering
UC Berkeley
2024.08 — 2025.12
Visiting Student
Chinese University of Hong Kong, Shenzhen
2022.09 — 2026.05
B.S. in Data Science

Fun

When I'm not coding, you'll probably find me:

  • photographing outdoors (insects are truly tiny wonders!)
  • getting lost in sci-fi (Time Debt is my excuse for pulling all-nighters)
  • locked in super cool modern origami (Hold Infinity in the palm of your hand)

Welcome!!! You've found my stash of some interesting projects~