Papers by Anni Zou
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning (2024.findings-acl)
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Xiangru Tang, Anni Zou, Zhuosheng Zhang, Ziming Li, Yilun Zhao, Xingyao Zhang, Arman Cohan, Mark Gerstein
| Challenge: | Large language models face unique challenges such as domain-specific terminologies and reasoning over specialized knowledge. |
| Approach: | They propose a multi-disciplinary collaboration framework that leverages LLM-based agents in a role-playing setting. |
| Outcome: | The proposed framework excels at mining and harnessing medical expertise within LLMs, as well as extending its reasoning abilities. |
Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification (2023.findings-acl)
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| Challenge: | Existing studies focus on grasping unstructured evidence or potential reasoning paths from structured knowledge bases, yet failing to exploit the benefits of heterogeneous knowledge simultaneously. |
| Approach: | They propose a commonsense fact verification model that bridging heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. |
| Outcome: | The proposed model can bridge heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. |
AuRoRA: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting (2024.lrec-main)
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| Challenge: | Existing methods for large language models (LLMs) lean on handcrafted or task-specific demonstrations and lack reliable knowledge base. |
| Approach: | They propose a one-for-all platform for augmented reasoning and refining based on chain-of-thought prompting that excels in adaptability, reliability, integrity, and interpretability. |
| Outcome: | The proposed system exhibits superior performances across six reasoning tasks and offers real-time visual analysis. |