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Zixuan Li

Zixuan Li has 7 papers in our library, in 2021–2026, most of them on Language models & pretraining, Information extraction, Dialogue systems; often with Long Bai, Jiafeng Guo, Xiaolong Jin.

7 papers in our library · 2021–2026

OpenAlex · h-index 1 · 7 works · 10 citations OpenAlex, 2026-10-14

📄 7 works in our library · 🔗 Cited 88 times in our library · ⭐ Paper Stars ranking: #7132

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

🎓Chinese University of Hong KongHong Kong · China2026 · 1 papercurrent🔬Institute of Computing TechnologyBeijing · China2026 · 1 papercurrent

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).Computer Science · 7 worksArtificial Intelligence · 7 worksArts and Humanities · 6 worksLanguage and Linguistics · 6 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 work

🧭 Topics 8Research subjects and languages the work is about.Language models & pretraining · 5 worksInformation extraction · 2 worksDialogue systems · 1 workEfficient models & compression · 1 workInformation retrieval & search · 1 workReasoning · 1 workLegal AI · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20263 papers20251 paper20241 paper20231 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024 · 68 citations)
    Universal Information Extraction (UIE) uses one model to pull entities, relations and events under many different schemas.
  2. Integrating Deep Event-Level and Script-Level Information for Script Event Prediction (2021 · 36 citations)
    The paper addresses script event prediction. Given a chain of events that have already occurred, the model must choose the next event from several candidates. The authors point out two shortcomings of earlier approaches. First, an event was usually reduced to a verb and the head words of its participants, so other…
  3. DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning (2023 · 27 citations)
    The order and choice of demonstrations in a prompt strongly affect how well large language models solve multi-step reasoning problems.
  4. Large Language Model-Based Event Relation Extraction with Rationales (2025 · 13 citations)
    The paper addresses document-level Event Relation Extraction (ERE), which covers temporal, causal, subevent and coreference links, using fine-tuned LLMs.
  5. Adam's Law: Textual Frequency Law on Large Language Models (2026 · 2 citations)
    The paper asks whether an LLM performs better when it receives a more common wording of the same content. The authors propose the Textual Frequency Law (TFL): among paraphrases with identical meaning, choose the one with the highest sentence-level frequency, both when prompting and when fine-tuning. Training corpora…
  6. Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026 · 2 citations)
    Memory modules let LLM agents personalize answers across long, multi-session conversations, but current systems handle time poorly.
  7. JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation (2026)
    Static benchmarks for LLMs saturate quickly, cover a fixed slice of knowledge, risk contamination, and pose questions whose difficulty may not suit the model under test.

 Co-authors here

12 of 34 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 88 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

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