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Yongxiang Li

Yongxiang Li has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Machine translation, Sentiment & emotion; often with Xuelong Li, Shuangyong Song, Ruiyu Fang.

4 papers in our library · 2024–2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 23 times in our library · ⭐ Paper Stars ranking: #8722

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

🏢China TelecomBeijing · China2026 · 4 paperscurrent🏢China TelecomChangsha · 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 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 workExperimental and Cognitive Psychology · 1 workPsychology · 1 workInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksSentiment & emotion · 1 workMachine translation · 1 workReasoning · 1 work

 Works by year

2020s

20263 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TeleChat Technical Report (2024 · 24 citations)
    TeleChat is a family of open bilingual (Chinese–English) large language models from China Telecom, with 3B, 7B and 12B parameters, released as base and chat versions.
  2. TeleAI at SemEval-2026 Task 3: Large Language Models for Dimensional Aspect-Based Sentiment Analysis (2026 · 1 citation)
    This paper describes TeleAI's entry for Track A, Subtask 1 (DimASR) of SemEval-2026 Task 3. In this task, sentiment toward each aspect term is not a polarity label but a pair of continuous Valence–Arousal (VA) scores between 1 and 9, scored with RMSE_VA. The authors treat the problem as direct regression. The input…
  3. CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning (2026)
    LLM-based translators often mishandle small but critical pieces of input: URLs, handles, HTML tags and emojis that must be copied exactly, and idioms, slang or culture-bound terms that need careful adaptation.
  4. TeleAI at SemEval-2026 Task 13: Data-Centric Full-Parameter Fine-Tuning with Multi-Level Ensembling for Generated Code Detection (2026)
    The paper describes TeleAI's entry to SemEval-2026 Task 13, which focuses on telling human-written source code apart from LLM-generated code. The task has three subtasks:

 Co-authors here

12 of 13 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 23 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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