Elamur Research⌕
☰
home / authors / Hongru Wang

Hongru Wang

Hongru Wang has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Dialogue systems, Evaluation & benchmarks; often with Cunxiang Wang, Liang Chen, Tat-Seng Chua.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 9 · 31 works · 408 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 173 times in our library · ⭐ Paper Stars ranking: #4152

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

🎓Hohai UniversityNanjing · China2025–2026 · 7 paperscurrent🎓Southeast UniversityNanjing · China2022–2025 · 9 papers🎓Chinese University of Hong KongHong Kong · Hong Kong2023–2025 · 6 papers🎓University of EdinburghEdinburgh · United Kingdom2024–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksDialogue systems · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20241 paperHongru Wang's papers of 202420231 paperHongru Wang's papers of 2023

 Papers

The most cited first (OpenAlex and our library).

  1. Knowledge Conflicts for LLMs: A Survey (2024 · 179 citations)
    This survey examines a recurring problem in large language models: the knowledge stored in a model's weights can disagree with information supplied at inference time, and supplied sources can disagree with each other.
  2. Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration (2023 · 128 citations)
    Chat assistants built on large language models, such as ChatGPT, respond fluently but mostly react to the user. They rarely ask for clarification when a request is ambiguous, and they seldom steer or resist a conversation. Deng et al. test whether such systems can handle proactive dialogue in three settings:

 Co-authors here

9 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 173 times (of the 141,840 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/hongru-wang/