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.
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
Affiliations
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 works
🧭 Topics 4Research subjects and languages the work is about.Coreference resolution · 1 workReasoning · 1 workLanguage models & pretraining · 1 workMachine translation · 1 work
Works by year
2020s
Papers
- 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. - 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… - 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. - 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
- 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
- Adapting Coreference Resolution Models through Active LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Michelle Yuan, Patrick Xia, Chandler May, et al.2022 · 35 citations
- MemInsight: Autonomous Memory Augmentation for LLM AgentsConference on Empirical Methods in Natural Language Processing (EMNLP)Rana Salama, Jason Cai, Michelle Yuan, et al.2025 · 22 citations
- Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and BandwidthConference of the European Chapter of the Association for Computational Linguistics (EACL)Michelle Yuan, Weiyi Sun, Amir H. Rezaeian, et al.2026 · 3 citations
- MT-OSC: Path for LLMs that Get Lost in Multi-Turn ConversationFindings of the Association for Computational LinguisticsJyotika Singh, Fang Tu, Miguel Ballesteros, et al.2026 · 3 citations
Co-authors here
- Yassine Benajiba ×3
- Jyotika Singh ×2
- Miguel Ballesteros ×2
- Sandip Ghoshal ×2
- Weiyi Sun ×2
- Anna Currey
- Benjamin Van Durme
- Chandler May
- Dan Roth
- Jason Cai
- Jordan Boyd-Graber
- Monica Sunkara
Venues
- Conference on Empirical Methods in Natural Language Processing (EMNLP) ×2: EMNLP 2025
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2022
- Conference of the European Chapter of the Association for Computational Linguistics (EACL): EACL 2026
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
Cited by
Updated
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