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Hao Wu

Hao Wu has 5 papers in our library, in 2020–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Bias, fairness & safety; often with Bin Chen, Shu-Tao Xia, Dan Roth.

5 papers in our library · 2020–2026

OpenAlex · h-index 0 · 5 works · 0 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 72 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6143

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

🎓Tsinghua UniversityBeijing · China2026 · 3 paperscurrent🎓Eastern Institute of Technology, NingboNingbo · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 5 worksArts and Humanities · 5 worksComputer Science · 5 worksLanguage and Linguistics · 5 worksDevelopmental and Educational Psychology · 1 workPsychology · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 6Research subjects and languages the work is about.English · 1 workQuestion answering · 1 workLanguage models & pretraining · 2 worksBias, fairness & safety · 2 worksEvaluation & benchmarks · 2 worksEfficient models & compression · 1 work

 Works by year

2020s

20264 papers20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions (2020 · 152 citations)
    TORQUE asks temporal relations as reading-comprehension questions instead of labelling event pairs with Allen-style relations.
  2. From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models (2026 · 1 citation)
    Most LLMs assume the entire input is available before they respond. This assumption fails for live speech, video, simultaneous translation or agents, where data arrives piece by piece and may be unbounded. Prior work labelled "streaming LLM" has mixed together quite different ideas: token-by-token decoding, chunked…
  3. C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts (2026)
    Most existing benchmarks for detecting machine-written text are built on English. The few that cover Chinese tend to rely on ChatGPT alone, use mostly QA-style texts, and pair them with artificial prompts. C-ReD addresses these gaps with a Chinese benchmark.
  4. Calibrating Inference Time Alignment with Sequence-level Risk Accumulation (2026)
    LLM defenses against jailbreaks often over-refuse. They block innocuous requests, such as how to "kill" a Linux process, because single tokens look dangerous. This paper presents SEAT, an inference-time decoding framework named for its approach of accumulating risk over whole sequences to calibrate alignment at test…
  5. When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language Models (2026)
    KV cache compression is now common in LLM serving because it saves memory and time, but its effect on safety alignment has received little attention.

All works

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

  1. TORQUE: A Reading Comprehension Dataset of Temporal Ordering QuestionsQiang Ning, Hao Wu, Rujun Han, et al.English · 152 citations
  2. From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language ModelsFindings of the Association for Computational LinguisticsJunlong Tong, Zilong Wang, YuJie Ren, et al.2026 · 1 citation
  3. C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World PromptsFindings of the Association for Computational LinguisticsChenxi Qing, Junxi Wu, Zheng Liu, et al.2026
  4. Calibrating Inference Time Alignment with Sequence-level Risk AccumulationAnnual Meeting of the Association for Computational Linguistics (ACL)Shanwen Tan, Ziyang Dong, Wei Ju, et al.2026
  5. When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoxiao Ma, Kuofeng Gao, Zeyi Lu, et al.2026
  6. FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetHao Wu et al.2026

 Co-authors here

12 of 19 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 72 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

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