Gaowen Liu
Gaowen Liu has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Jayanth Srinivasa, Dawn Song, Kaiwen Zhou.
OpenAlex · ORCID · Wikidata · h-index 10 · 72 works · 522 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 38 times in our library · ⭐ Paper Stars ranking: #7990
Affiliations
Fields, topics and concepts
📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksSafety Research · 1 workSocial Sciences · 1 workComputer Vision and Pattern Recognition · 1 work
🧭 Topics 4Research subjects and languages the work is about.Bias, fairness & safety · 2 worksReasoning · 2 worksLanguage models & pretraining · 2 worksEvaluation & benchmarks · 1 work
Works by year
2020s
Papers
- The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1 (2025 · 30 citations)
Open-weight large reasoning models such as DeepSeek-R1 have become much stronger. Because they are freely available, misuse is a real concern. This study audits their safety systematically. It compares three open reasoning models (DeepSeek-R1, the Llama 3.3-based distilled DeepSeek-R1-70b, and QwQ) with non-reasoning… - SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning (2025 · 9 citations)
Large Reasoning Models (LRMs) such as DeepSeek-R1 deliberate before answering, but they often comply with harmful requests and jailbreaks. - Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection (2026 · 4 citations)
The paper asks how well language models can absorb new facts after pre-training. Earlier knowledge-injection studies mostly relied on synthetic data drawn from noisy Wikipedia snapshots, and their questions were often poorly specified. - FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments (2026)
LLM-driven agents in multi-turn customer-service tool-use settings often fail because early wrong decisions compound over the conversation.
Co-authors here
- Jayanth Srinivasa ×4
- Dawn Song ×2
- Kaiwen Zhou ×2
- Xin Eric Wang ×2
- Xuandong Zhao ×2
- Ali Payani
- Amir Saeidi
- Aosong Feng
- Chitta Baral
- Jingbo Shang
- Letian Peng
- Shangbin Feng
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- International Joint Conference on Natural Language Processing (IJCNLP): IJCNLP 2025
Cited by
Updated
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