Junda Wu
Junda Wu has 7 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning, Vision & language; often with Julian McAuley, Yu Xia, Tong Yu.
OpenAlex · ORCID · Wikidata · h-index 14 · 58 works · 507 citations OpenAlex, 2026-10-14
📄 7 works in our library · 🔗 Cited 46 times in our library · ⭐ Paper Stars ranking: #7348
Affiliations
Fields, topics and concepts
📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Computer Science · 7 worksArts and Humanities · 6 worksComputer Vision and Pattern Recognition · 4 worksArtificial Intelligence · 6 worksLanguage and Linguistics · 6 worksSignal Processing · 1 workDecision Sciences · 1 workManagement Science and Operations Research · 1 workInformation Systems · 2 works
🧭 Topics 5Research subjects and languages the work is about.Language models & pretraining · 3 worksReasoning · 2 worksVision & language · 2 worksQuestion answering · 1 workEvaluation & benchmarks · 1 work
Works by year
2020s
Papers
- A Survey of Small Language Models (2024 · 31 citations)
Large language models perform well but need heavy compute, memory and data, so they usually run on centralized, specialized hardware. - From Selection to Generation: A Survey of LLM-based Active Learning (2025 · 22 citations)
Active learning (AL) traditionally lowers labeling effort by picking the most useful unlabeled examples with metrics such as uncertainty or diversity. - WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning (2025 · 10 citations)
Multimodal LLMs perform well on many vision-language tasks, but it is unclear whether they can reason about sheet music. - Doc-React: Multi-page Heterogeneous Document Question-answering (2025 · 6 citations)
Doc-React tackles question answering over long documents whose pages mix text, charts and images in irregular layouts. - CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing (2026)
The paper addresses indirect prompt injection, where an LLM obeys commands hidden inside supplied context (e.g., a retrieved passage) instead of answering the user's actual request. - Evaluating Language Model Pluralism through In-the-wild Crowd Discussions (2026)
LLMs tuned with preference alignment often lean toward particular stances on subjective questions. Yet pluralism has mostly been tested with multiple-choice or rating formats, and the few open-ended benchmarks depend on costly crowd annotation for every new domain. The authors propose PluralEval, which builds… - SceneAlign: Aligning Multimodal Reasoning to Scene Graphs in Complex Visual Scenes (2026)
Multimodal LLMs often produce rationales that sound convincing but are not tied to what the image actually shows.
Co-authors here
- Julian McAuley ×5
- Yu Xia ×4
- Tong Yu ×3
- Gagan Mundada ×2
- Joe Barrow ×2
- Lina Yao ×2
- Ruiyi Zhang ×2
- Ryan A. Rossi ×2
- Ryan Aponte ×2
- Xintong Li ×2
- Zhouhang Xie ×2
- Bodhisattwa Prasad Majumder
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×5: ACL 2026 ×3, ACL 2025 ×2
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
Cited by
Updated
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