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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.

7 papers in our library · 2024–2026

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

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. 2 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

 Papers

The most cited first (OpenAlex and our library).

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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…
  7. 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

12 of 30 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 46 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

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