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Dawei Li

Dawei Li has 6 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Huan Liu, Zhen Tan, Bohan Jiang.

6 papers in our library · 2024–2026

OpenAlex · h-index 2 · 5 works · 32 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 190 times in our library · ⭐ Paper Stars ranking: #4373

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

🎓Arizona State UniversityTempe · United States2012–2026 · 8 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 6 worksComputer Science · 6 worksArtificial Intelligence · 6 worksLanguage and Linguistics · 6 worksComputer Vision and Pattern Recognition · 3 worksInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 4 worksReasoning · 4 worksEfficient models & compression · 2 worksKnowledge graphs & ontologies · 1 work

 Works by year

2020s

20262 papers20251 paper20243 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models for Data Annotation and Synthesis: A Survey (2024 · 262 citations)
    Labeling or generating training data by hand is slow, expensive and often requires domain experts. This survey examines how LLMs such as GPT-4, Gemini and LLaMA-2 can automate that work. It is a literature review, so it reports no new experiments. The authors frame the setting with two roles: an annotator model A…
  2. Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning (2024 · 57 citations)
    The paper asks whether large language models can improve their reasoning by learning from their own errors, rather than only imitating gold-standard Chain-of-Thought (CoT) rationales.
  3. Contextualization Distillation from Large Language Model for Knowledge Graph Completion (2024 · 30 citations)
    Pre-trained language models (PLMs) used for knowledge graph completion (KGC) depend on textual side information, such as Wikipedia articles or synset definitions.
  4. Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens (2026 · 21 citations)
    The paper asks why chain-of-thought (CoT) prompting sometimes yields convincing step-by-step reasoning and sometimes collapses.
  5. DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models (2025 · 21 citations)
    Large Reasoning Models tend to "overthink": they produce very long chains of thought full of redundant checks and backtracking, which wastes compute.
  6. Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning (2026 · 7 citations)
    Reasoning LLMs often keep producing chain-of-thought steps long after the answer is effectively settled. This "overthinking" wastes compute and can even push the model toward a wrong answer. The authors present REFRAIN, a framework that needs no training and decides at inference time when to cut reasoning short. It…

 Co-authors here

12 of 17 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 190 times (of the 149,439 papers whose bibliographies we read); 49 citing papers with a page here →.

Updated

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