281 citations · 475 across the 19 of their papers we have counts for
21 papers
FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents
Yuhao Zhang, O. Ozan Koyluoglu, Thejas Venkatesh +4
AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow. Existing benchmarks mainly target…
Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search
Howard Yen, Yoonsang Lee, Ashwin Paranjape +5
Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, enabling powerful applications like deep…
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
Orion Weller, Benjamin Van Durme, Dawn Lawrie +3
Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we pre…
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models
Zhengxuan Wu, Yuhao Zhang, Peng Qi +6
Modern language models (LMs) need to follow human instructions while being faithful; yet, they often fail to achieve both. Here, we provide concrete evidence of a trade-off between…
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
Rujun Han, Yuhao Zhang, Peng Qi +6
Question answering based on retrieval augmented generation (RAG-QA) is an important research topic in NLP and has a wide range of real-world applications. However, most existing da…
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
Xingyu Fu, Sheng Zhang, Gukyeong Kwon +10
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge. Recently, pre-trained Langua…
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks
Kaiser Sun, Peng Qi, Yuhao Zhang +3
Generative models have been widely applied to solve extractive tasks, where parts of the input is extracted to form the desired output, and achieved significant success. For exampl…
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
Saahil Jain, Ashwin Agrawal, Adriel Saporta +9
Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In…
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
Yasuhide Miura, Yuhao Zhang, Emily Bao Tsai +2
Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying po…
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Yuhao Zhang, Hang Jiang, Yasuhide Miura +2
Learning visual representations of medical images (e.g., X-rays) is core to medical image understanding but its progress has been held back by the scarcity of human annotations. Ex…
Do Syntax Trees Help Pre-trained Transformers Extract Information?
Devendra Singh Sachan, Yuhao Zhang, Peng Qi +1
Much recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. However, the effect of incorporating dependency…
Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
Yuhao Zhang, Yuhui Zhang, Peng Qi +2
We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabil…
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Peng Qi, Yuhao Zhang, Yuhui Zhang +2
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Stanza features a language-…
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations
Peng Qi, Yuhao Zhang, Christopher D. Manning
We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge…
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
Yuhao Zhang, Derek Merck, Emily Bao Tsai +2
Neural abstractive summarization models are able to generate summaries which have high overlap with human references. However, existing models are not optimized for factual correct…
Universal Dependency Parsing from Scratch
Peng Qi, Timothy Dozat, Yuhao Zhang +1
This paper describes Stanford's system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks require…
Learning to Summarize Radiology Findings
Yuhao Zhang, Daisy Yi Ding, Tianpei Qian +2
The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians. Howev…
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Yuhao Zhang, Peng Qi, Christopher D. Manning
Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., neg…
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Jinfeng Rao, Wei Yang, Yuhao Zhang +2
Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have only been applied to standard ad hoc retrieval tasks over web p…
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Xuan Wang, Yu Zhang, Xiang Ren +5
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and dise…
Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings
Yuhao Zhang, Sandeep Ayyar, Long-Huei Chen +1
Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the develop…