Tianzhe Zhao

Tianzhe Zhao has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Evaluation & benchmarks; often with Erik Cambria, Fangzhi Xu, Jiawei Han.

2 papers in our library · 2023–2026

The profile is the library's match of the name; two people with one name are not always told apart.

Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

Of its works in our library, with the number of works on each.

The most cited first (OpenAlex and our library).

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond (2023 · 142 citations)
The paper asks whether current LLMs reason logically or mostly rely on surface patterns and memorized benchmark content.
G-HiRel: Enhancing the Adaption to Knowledge Updating for Large Language Model Reasoning (2026)
Large language models struggle when facts change after training: retraining or editing weights is expensive and scales poorly, and in-context injection of edited facts often fails on multi-hop questions.

7 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 70 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated 2 November 2026