Papers by Fenglin Yin

5 papers
Contrastive Attention for Automatic Chest X-ray Report Generation (2021.findings-acl)

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Challenge: Recent studies show that learning-based models fail to accurately capture and describe abnormal regions due to data bias.
Approach: They propose a model that compares the current input image with normal images to capture abnormal regions by contrasting the input image and normal images.
Outcome: The proposed model can be easily incorporated into existing models to boost their performance under most metrics.
DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)

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Challenge: commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools .
Approach: They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression .
Outcome: The proposed approach outperforms human experts in medical examinations on diverse datasets.
Conversing with databases: Practical Natural Language Querying (2023.emnlp-industry)

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Challenge: Large amount of companies' data is stored in relational databases . quick hypotheses validation is rarely, if ever, possible for majority of nontechnical business stakeholders.
Approach: They propose a hybrid NLQ system for conversational DB querying that allows non-technical users to formulate data requests as natural language questions.
Outcome: The proposed system is based on a hybrid NLQ (Natural Language Querying) system for conversational DB querying.
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels (2023.findings-acl)

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Challenge: Existing approaches to generate informative titles for products with limited labels are inadequate for novel products.
Approach: They propose a prompt-based approach to generate attractive titles for novel products . they use multimodal prompts to preserve characteristics and writing styles of novel products.
Outcome: The proposed approach achieves state-of-the-art results on novel product categories with limited labels.
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

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Challenge: Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach: They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome: The proposed model outperforms human experts in multiple medical tasks.

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