Aiwei Liu
Aiwei Liu has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Code & programming languages; often with Lijie Wen, Leyi Pan, Philip S. Yu.
OpenAlex · ORCID · Wikidata · h-index 15 · 71 works · 611 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 83 times in our library · ⭐ Paper Stars ranking: #6160
Affiliations
Fields, topics and concepts
📚 Shelves 10The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 3 worksComputer Networks and Communications · 1 workComputer Vision and Pattern Recognition · 1 workDecision Sciences · 1 workHardware and Architecture · 1 workInformation Systems · 1 workStatistics, Probability and Uncertainty · 1 work
🧭 Topics 5Research subjects and languages the work is about.Code & programming languages · 1 workEvaluation & benchmarks · 1 workSemantic parsing & meaning representation · 1 workLanguage models & pretraining · 2 worksEfficient models & compression · 1 work
Works by year
2020s
Papers
- A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability (2023 · 127 citations)
The paper asks how well ChatGPT turns natural-language questions into executable SQL with no task-specific training. - Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation? (2025 · 11 citations)
Watermarking LLM outputs is promoted as a way to catch unauthorized knowledge distillation. It relies on "radioactivity": student models trained on watermarked teacher text inherit a detectable signal. This paper tests whether that signal survives a deliberate attacker. The authors propose three removal attacks: - ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary (2025 · 11 citations)
Writing the comparative part of a literature review is slow, and earlier LLM-based review tools mostly targeted retrieval and screening. - d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models (2026 · 3 citations)
Reinforcement learning for diffusion large language models (dLLMs) has two weak spots. Rewards are usually sparse outcome scores or unverified process signals, and per-token log-probabilities are approximated without accounting for the expectation over all possible decoding orders. d-TreeRPO addresses both. It…
Co-authors here
- Lijie Wen ×4
- Leyi Pan ×2
- Philip S. Yu ×2
- Xuming Hu ×2
- Bolin Ding
- Irwin King
- Kai Yu
- Liancheng Fang
- Lu Chen
- Shiyu Huang
- Shuchang Tao
- Zhaoyang Liu
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026, ACL 2025
- International Conference on Computational Linguistics (COLING): COLING 2025
Cited by
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