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Michelle Yuan

Michelle Yuan has 4 papers in our library, in 2022–2026, most of them on Coreference resolution, Machine translation, Language models & pretraining; often with Yassine Benajiba, Jyotika Singh, Miguel Ballesteros.

4 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 7 · 18 works · 296 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 128 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7466

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. 4 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. Adapting Coreference Resolution Models through Active Learning (2022 · 35 citations)
    Neural coreference resolution (CR) systems trained on OntoNotes 5.0 often perform poorly in new domains, where text style, entity meanings and annotation guidelines differ.
  2. MemInsight: Autonomous Memory Augmentation for LLM Agents (2025 · 22 citations)
    LLM agents rely on long-term memory to give coherent, personalized answers. As stored interactions pile up, raw and unstructured memory becomes noisy and harder to search. MemInsight addresses this by having an LLM backbone enrich each memory entry automatically with attribute–value annotations, without a…
  3. Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth (2026 · 3 citations)
    The survey asks why transformer models, despite strong results in language and vision, remain unreliable at exact symbolic work such as arithmetic, Boolean logic, parsing, and multi-step algorithmic composition.
  4. MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation (2026 · 3 citations)
    LLMs perform noticeably worse when a task's instructions and context are spread across several dialogue turns.

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Cold-start Active Learning through Self-supervised Language Modeling ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetMichelle Yuan, Hsuan-Tien Lin, Jordan Boyd-Graber2020 · 229 citations

 Co-authors here

12 of 16 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 128 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

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