Papers by Aidong Zhang
Benchmarking Retrieval-Augmented Generation for Medicine (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have state-of-the-art performance on a wide range of medical question answering tasks, but they still face challenges with hallucinations and outdated knowledge. |
| Approach: | They propose a benchmark to evaluate medical RAG systems using large-scale experiments with over 1.8 trillion prompt tokens. |
| Outcome: | The proposed benchmark improves accuracy of six different LLMs by up to 18% over chain-of-thought prompting. |
InfAL: Inference Time Adversarial Learning for Improving Research Ideation (2025.findings-emnlp)
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| Challenge: | Advancements in Large Language Models (LLMs) have opened new opportunities for scientific discovery by assisting researchers in generating novel hypotheses and ideas. |
| Approach: | They propose an inference time adversarial learning approach that optimizes the utilization of LLMs’ parametric knowledge without additional model training. |
| Outcome: | The proposed approach optimizes the utilization of LLMs’ parametric knowledge without requiring additional model training, making adversarial learning efficient and context-driven. |
COCO-Tree: Compositional Hierarchical Concept Trees for Enhanced Reasoning in Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to improve compositional reasoning in vision language models are resource-intensive or do not provide an interpretable reasoning process. |
| Approach: | They propose a method that augments VLM outputs with carefully designed neurosymbolic concept trees learned from LLMs to improve VLM’s linguistic reasoning. |
| Outcome: | Empirical results show that COCO-Tree significantly improves compositional generalization and provides a rationale behind VLM predictions. |
MedCite: Can Language Models Generate Verifiable Text for Medicine? (2025.findings-acl)
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| Challenge: | Existing LLM-based medical question answering systems lack citation generation and evaluation capabilities, raising concerns about their adoption in practice. |
| Approach: | They propose a framework that facilitates the design and evaluation of LLM citations for medical tasks and a retrieval-citation method that generates high-quality citation. |
| Outcome: | The proposed method achieves superior citation precision and recall improvements compared to strong baseline methods and correlates well with annotation results from professional experts. |
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval (2023.findings-emnlp)
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| Challenge: | Visually-rich document entity retrieval (VDER) is an important topic in industrial NLP applications. |
| Approach: | They propose a task-aware meta-learning framework to tackle the problem of visually-rich document entity retrieval (VDER) they adopt a hierarchical decoder and employ contrastive learning to achieve this goal. |
| Outcome: | The proposed framework significantly improves the robustness of popular meta-learning baselines. |