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Junjue Wang

Junjue Wang has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Multilingual & cross-lingual NLP; often with Heli Qi, Qingcheng Zeng, Weihao Xuan.

3 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 28 · 85 works · 3,581 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 74 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7006

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. 1 more institution 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

 Papers

The most cited first (OpenAlex and our library).

  1. MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025 · 75 citations)
    Evaluating LLMs across languages has two gaps. Demanding reasoning benchmarks such as MMLU-Pro exist mainly in English. Multilingual suites like Global-MMLU combine translation sources of uneven quality and pose easier questions. The authors address both with MMLU-ProX, which carries the ten-option, reasoning-heavy…
  2. Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models (2025 · 11 citations)
    Vision-language models (VLMs) can state in plain language how sure they are of an answer. This study asks whether that stated confidence actually tracks how often they are correct. The authors evaluate verbalized calibration across three model families: instruction-tuned, text-reasoning, and visual-reasoning…
  3. The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents (2026 · 2 citations)
    The paper asks whether tool use always worsens how well LLM agents' stated confidence matches their real accuracy, or whether the effect depends on the tool.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery ↗IEEE Transactions on Pattern Analysis and Machine Intelligence · no summary yetZhuo Zheng et al.2023 · 93 citations

 Co-authors here

9 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 74 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

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