Papers by Shengyao Zhuang
VISA: Retrieval Augmented Generation with Visual Source Attribution (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to retrieval-augmented generation primarily link generated content to document-level references, making it difficult for users to locate evidence among multiple content-rich retrieved documents. |
| Approach: | They propose a novel approach that combines answer generation with visual source attribution by leveraging large vision-language models to identify evidence and highlight exact regions that support the generated answers with bounding boxes in the retrieved document screenshots. |
| Outcome: | The proposed approach identifies evidence and highlights exact regions that support the generated answers with bounding boxes in the retrieved document screenshots. |
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that large language models (LLMs) rank documents based on the probability of generating the query given the content of a document. |
| Approach: | They propose a ranking system that integrates LLMs with a hybrid zero-shot retriever. |
| Outcome: | The proposed system shows exceptional ranking in both zero-shot and few-shot scenarios. |
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) excel in zero-shot document ranking tasks. |
| Approach: | They propose a prompt-based re-ranking method that requires no further training but is only feasible for reranking a handful of candidates due to computational costs. |
| Outcome: | The proposed method can retrieve documents from the entire corpus without training and with a large amount of paired text data. |
The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It (2025.acl-long)
Copied to clipboard
| Challenge: | Traditionally, CXR report generation relies on data from a patient’s exam, overlooking valuable information from patient electronic health records. |
| Approach: | They propose to integrate patient data from ED records into multimodal language models that embed patient data into a language model. |
| Outcome: | The proposed model incorporates patient data from the MIMIC-CXR and MIMICIV-ED datasets to improve diagnostic accuracy and improves radiologist effectiveness. |
Dealing with Typos for BERT-based Passage Retrieval and Ranking (2021.emnlp-main)
Copied to clipboard
| Challenge: | Current approaches to passage retrieval and ranking rely on pre-trained deep language models that model the semantic matching between queries and passages. |
| Approach: | They propose a typos-aware training framework for DR and BERT to address this issue. |
| Outcome: | The proposed models respond and adapt to keyword typos occurring in queries, and significantly improve their retrieval and ranking effectiveness. |
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)
Copied to clipboard
Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin
| Challenge: | Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors . |
| Approach: | They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents. |
| Outcome: | The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries. |