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Shu Wu

Shu Wu has 4 papers in our library, in 2025–2026, most of them on Bias, fairness & safety, Text generation, Evaluation & benchmarks; often with Qiang Liu, Dingkun Long, Jinghao Zhang.

4 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 40 · 286 works · 10,703 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 112 times in our library · 6 more in our library without a page yet · ⭐ Paper Stars ranking: #9590

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. 3 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. Personalized Text Generation with Contrastive Activation Steering (2025 · 13 citations)
    The paper addresses personalized text generation: producing outputs, such as emails or headlines, that match an individual user's writing style as shown in their past texts.
  2. Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing (2026)
    Existing Composed Image Retrieval (CIR) benchmarks such as CIRR, FashionIQ and CIRCO cover few modification types and have overlapping category labels.
  3. RLShield: Dynamic Jailbreak Detection for LLMs via Reinforced Adaptive Learning (2026)
    Jailbreak detectors for LLMs usually rely on fixed decision thresholds or fixed internal components. This makes them brittle when attack styles change or the underlying model is updated. RLShield treats threshold choice as a continuous control problem and learns a separate cut-off for each prompt. The framework has…
  4. When Safety Alignment Fails to Generalize: Probing with Language Game Jailbreaks (2026)
    Safety-aligned LLMs are supposed to refuse harmful requests. This paper asks whether those refusals depend on what a prompt means or only on how it looks. Long et al. probe this with "language games": rule-based rewritings of text, such as inserting a syllable before each vowel, that change a prompt's appearance…

All works

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

  1. Session-Based Recommendation with Graph Neural Networks ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetShu Wu et al.2019 · 1,561 citations
  2. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetQiang Liu et al.2016 · 982 citations
  3. A Convolutional Approach for Misinformation Identification ↗International Joint Conference on Artificial Intelligence · no summary yetFeng Yu et al.2017 · 526 citations
  4. Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetYufeng Zhang et al.2020 · 351 citations
  5. TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetFeng Yu et al.2020 · 231 citations
  6. Reinforcement Learning–Guided Adaptive Tuning for Out-of-Distribution Harmful Text Detection ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetMengyu Xiang et al.2026

 Co-authors here

12 of 13 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 112 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

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