Papers by Anna Cai
MemInsight: Autonomous Memory Augmentation for LLM Agents (2025.emnlp-main)
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| Challenge: | Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. |
| Approach: | They propose an autonomous memory augmentation approach to enhance semantic data representation and retrieval mechanisms by leveraging historical interactions. |
| Outcome: | The proposed approach outperforms a baseline RAG by 34% in recall for LoCoMo retrieval on three task scenarios and boosts persuasiveness of recommendations by 14%. |
Counting the Bugs in ChatGPT’s Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model (2023.emnlp-main)
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Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schuetze, Kemal Oflazer, David Mortensen
| Challenge: | Existing studies on large language models (LLMs) ignore the remarkable ability of humans to generalize and focus only on English. |
| Approach: | They conduct the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages. |
| Outcome: | The proposed model massively underperforms purpose-built systems, particularly in English. |
Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data (2021.acl-long)
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Paul Pu Liang, Terrance Liu, Anna Cai, Michal Muszynski, Ryo Ishii, Nick Allen, Randy Auerbach, David Brent, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Mental health conditions remain underdiagnosed in many countries despite access to advanced medical care . a new approach to learn mood markers from mobile data is needed to improve accuracy and improve learning from typed text. |
| Approach: | They propose to use mobile data to learn mood markers without identifying users through personal or protected attributes. |
| Outcome: | The proposed model obfuscates user identities while remaining predictive . future directions include better models and pre-learning from typed text . |