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Yuchen Liu

Yuchen Liu has 5 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Question answering; often with Bihui Yu, Chen Wang, Chengqing Zong.

5 papers in our library · 2023–2026

OpenAlex · h-index 0 · 5 works · 0 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 75 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6920

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 Academy of SciencesBeijing · China2026 · 2 paperscurrent🔬Institute of AutomationBeijing · China2026 · 2 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2026 · 2 paperscurrent🏢XiaomiBeijing · 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 4 works

🧭 Topics 7Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 worksSpeech & spoken language · 1 workEvaluation & benchmarks · 1 workInformation extraction · 1 workQuestion answering · 1 workVision & language · 1 work

 Works by year

2020s

20263 papers20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models (2023 · 61 citations)
    Tool-using language systems such as ReAct alternate between model reasoning and tool calls. Each time a tool returns a result, the model must be called again with the full history: instructions, examples, earlier thoughts and observations. Because commercial LLM APIs are stateless and priced per token, this repeated…
  2. BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing (2023 · 32 citations)
    Text-trained large language models struggle to take speech as input. Cascaded systems that feed ASR output into an LLM keep the two components separate, and end-to-end speech LLMs depend on speech instruction data, which is rare. The authors first show that training an adapter only to reproduce transcripts makes the…
  3. GenProve: Learning to Generate Text with Fine-Grained Provenance (2026)
    Citations in LLM answers often point only to whole documents. They do not show which sentence backs a claim or how that sentence is used, so checking the answer stays hard. The authors define a task called Generation-time Fine-grained Provenance. The model writes an answer and, for each sentence, emits structured…
  4. M³-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering (2026)
    Most knowledge-based VQA benchmarks test coarse categories or one main entity and need only one or two reasoning steps.
  5. ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction (2026)
    Many LLM-based universal information extraction (UIE) systems add extra signals on top of the standard annotations, such as schema hints, external knowledge, guidelines, or verification pipelines.

All works

6 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

  1. ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language ModelsarXiv:2305.18323Binfeng Xu, Zhiyuan Peng, Bowen Lei, et al.2023 · 61 citations
  2. BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation WritingarXiv:2309.00916Chen Wang, Minpeng Liao, Zhongqiang Huang, et al.2023 · 32 citations
  3. GenProve: Learning to Generate Text with Fine-Grained ProvenanceAnnual Meeting of the Association for Computational Linguistics (ACL)Jingxuan Wei, Xingyue Wang, Yanghaoyu Liao, et al.2026
  4. M³-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Jiatong Ma, Longteng Guo, Yuchen Liu, et al.2026
  5. ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information ExtractionFindings of the Association for Computational LinguisticsWenda Liu, Song Zhigang, Shuai Nie, et al.2026
  6. Detecting AI-Generated Content on Social Media with Multi-modal Language Models ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · no summary yetChenyang Yang et al.2026

 Co-authors here

12 of 14 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 75 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

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