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Tao Ji

Tao Ji has 2 papers in our library, in 2019–2025, most of them on Dependency parsing, Language models & pretraining; often with Caishuang Huang, Junjie Ye, Man Lan.

2 papers in our library · 2019–2025

OpenAlex · ORCID · h-index 15 · 96 works · 816 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 143 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6460

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

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 15 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

 Works by year

2020s

2010s

 Papers

The most cited first (OpenAlex and our library).

  1. Graph-based Dependency Parsing with Graph Neural Networks (2019 · 94 citations)
    Graph-based dependency parsers score every possible head–dependent pair and then decode a tree. Their accuracy depends heavily on how each word is represented. Earlier encoders, such as BiLSTMs and the biaffine parser of Dozat and Manning (2017), mostly capture first-order parent–child information. Ji, Wu and Lan add…
  2. ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios (2025 · 35 citations)
    Most benchmarks for tool use by large language models only check whether a model picks the expected API. That approach works only when correct answers can be fixed in advance, and it reveals little about the skills behind successful tool use. ToolEyes addresses both gaps. The authors define seven realistic scenarios…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Secrets of RLHF in Large Language Models Part II: Reward Modeling ↗arXiv:2401.06080 · no summary yetBinghai Wang et al.2024 · 72 citations
  2. StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback ↗Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetShihan Dou et al.2024 · 32 citations

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 143 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

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