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Hao Wang

5 papers hereh-index 4108 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CR1
same name
  • Hao Wang — 79 papers, h 101
  • Hao Wang — 34 papers, h 18
  • Hao Wang — 33 papers, h 13
  • Hao Wang — 24 papers, h 7
  • Hao Wang — 23 papers, h 9
  • Hao Wang — 22 papers, h 48

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Hallucination-R1: Robustness-Oriented Paraphrase Generation for Factual Consistency

Wenhan Yu, Wenxin Wu, Hao Wang +1

Factual hallucination is commonly defined by incorrect factual outputs. We study a paraphrase-induced hallucination setting, where a model answers a factual question correctly in i…

cs.CL2026

From Synthetic to Native: Benchmarking Multilingual Intent Classification in Logistics Customer Service

Haoyu He, Jinyu Zhuang, Haoran Chu +5

Multilingual intent classification is central to customer-service systems on global logistics platforms, where models must process noisy user queries across languages and hierarchi…

cs.CL2024

Harnessing the Plug-and-Play Controller by Prompting

Hao Wang, Lei Sha

Controllable text generation is a growing field within natural language generation (NLG) that focuses on producing text that meets specific constraints in real-world applications.…

cs.CL2024★ 1 cited

ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings

Hao Wang, Hao Li, Minlie Huang +1

The safety defense methods of Large language models(LLMs) stays limited because the dangerous prompts are manually curated to just few known attack types, which fails to keep pace…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.