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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.

4 papers in our library · 2023–2026

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

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. 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.
  2. 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:
  3. 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.
  4. 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

12 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 83 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

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