Mengru Wang
Mengru Wang has 5 papers in our library, in 2024–2026, most of them on Language models & pretraining, Bias, fairness & safety, Evaluation & benchmarks; often with Huajun Chen, Ningyu Zhang, Shumin Deng.
OpenAlex · h-index 0 · 3 works · 0 citations OpenAlex, 2026-10-14
📄 5 works in our library · 🔗 Cited 75 times in our library · ⭐ Paper Stars ranking: #6792
Affiliations
Fields, topics and concepts
📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 5 worksArts and Humanities · 5 worksComputer Science · 5 worksLanguage and Linguistics · 5 worksEconomics and Econometrics · 1 workEconomics, Econometrics and Finance · 1 workSafety Research · 1 workSocial Sciences · 1 work
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 4 worksBias, fairness & safety · 2 worksEvaluation & benchmarks · 1 work
Works by year
2020s
Papers
- Detoxifying Large Language Models via Knowledge Editing (2024 · 84 citations)
Safety-aligned LLMs can still be steered into harmful output when a malicious request is wrapped in a carefully engineered jailbreak prompt. The authors ask whether knowledge editing can fix the specific toxic parts of a model instead of only masking them. - Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms (2025 · 14 citations)
The paper addresses how to control LLM behavior at inference time, especially safety, without relying on fragile system prompts. - Too Good to be Bad: On the Failure of LLMs to Role-Play Villains (2026 · 3 citations)
LLMs are increasingly used for interactive fiction, games and co-written stories, yet nobody had systematically checked whether they can convincingly play antagonists. - Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026 · 1 citation)
Methods for controlling LLM behaviour are usually studied separately, with different assumptions and evaluation setups. - Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026)
LLMs are increasingly used in consequential settings, but little is known about the values they apply when distributing scarce resources.
Co-authors here
- Huajun Chen ×3
- Ningyu Zhang ×3
- Shumin Deng ×3
- Ziwen Xu ×3
- Fanghua Ye ×2
- Qu Yang ×2
- Ruotian Ma ×2
- Xingyu Chen ×2
- Yunzhi Yao ×2
- Zhaopeng Tu ×2
- Haiwen Hong
- Hui Xue
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2026, ACL 2025, ACL 2024
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
Cited by
Updated
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