Natural Language Processing · Large Language Models · Interpretability
Linfeng Liu
I am a PhD student in the College of Engineering and Applied Science at the University of Cincinnati, advised by Prof. Tianyu Jiang. My research focuses on natural language processing, large language models, machine translation, multimodality, and interpretability.
My current work studies how modern AI systems handle non-compositional language, multimodal associative reasoning, and model-question interactions in LLM evaluation.
Research Interests
- Natural language processing
- Large language models
- Machine translation
- Multimodality
- Interpretability
Experience
- University of Cincinnati, PhD student, 2025-present
- University of Cincinnati, MS student, 2024-2025
- University of Cincinnati, undergraduate student, 2019-2024
- Chongqing University, undergraduate student, 2019-2024
Selected Publications
Evaluating the Impact of Verbal Multiword Expressions on Machine Translation
Linfeng Liu, Saptarshi Ghosh, Tianyu Jiang
ACL 2026 Main Conference
Evaluates how verbal multiword expressions affect machine translation quality across seven language pairs and eight machine translation systems.
evaluation · multiword expressions · machine translation
A Computational Approach to Visual Metonymy
Saptarshi Ghosh, Linfeng Liu, Tianyu Jiang
EACL 2026 Main Conference Oral
Introduces ViMET, a visual metonymy benchmark for testing whether vision-language models can interpret indirect associative cues rather than only literal depiction.
metonymy · semiotic theory · dataset · cognitive reasoning
A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory
Louie Hong Yao, Nicholas Jarvis, Tiffany Zhan, Saptarshi Ghosh, Linfeng Liu, Tianyu Jiang
Presents JE-IRT, a geometric item-response framework where direction captures semantics and norm captures difficulty for interpretable LLM evaluation.
large language models · interpretability · item response theory · joint embeddings
Collaborators
I work with Prof. Tianyu Jiang, my PhD advisor at the University of Cincinnati on natural language processing, large language models, machine translation, multimodality and interpretability.