Papers by Alexa Siu
A Survey on LLM-based Conversational User Simulation (2026.eacl-long)
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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi
| Challenge: | Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation. |
| Approach: | They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments . |
| Outcome: | The proposed model enables high-fidelity generation of synthetic user conversation. |
ATLAS: A System for PDF-centric Human Interaction Data Collection (2024.naacl-demo)
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| Challenge: | Recent advances in AI only make the importance of high-quality data more pronounced. |
| Approach: | They propose to use the Portable Document Format (PDF) as a data format to better support researchers in collecting rich PDF-centric datasets from users. |
| Outcome: | The proposed toolkit and extensible schema allows researchers to customize the data collection tasks for a variety of purposes, including annotations, drawing, and reading behavior analytics. |
PDFTriage: Question Answering over Long, Structured Documents (2024.emnlp-industry)
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| Challenge: | Existing approaches to document QA use a pre-retrieval step to retrieve the relevant context from documents, but this is incongruous with the user's mental model of the document. |
| Approach: | They propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content. |
| Outcome: | The proposed approach can retrieve context based on structure or content across several classes of questions where existing retrieval-augmented LLMs fail. |
MATSA: Multi-Agent Table Structure Attribution (2024.emnlp-demo)
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| Challenge: | Tabular data present unique challenges for attribution due to ambiguities, complex header hierarchies, and the difficulty in interpreting individual table cells without row and column context. |
| Approach: | They propose a task to generate row and column-level attributions supporting LLM-generated answers. |
| Outcome: | The proposed task outperforms baselines on tabCite and improves F1 score. |
AnalystBench: Benchmarking professional long-form report generation with web-mined multimodal tasks (2026.findings-acl)
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Chau Minh Pham, Zichao Wang, Puneet Mathur, Alexa Siu, Akriti Jain, Aparna Garimella, Ananya B. Sai, Nedim Lipka, Mohit Iyyer, Varun Manjunatha
| Challenge: | Existing benchmarks decompose the end-to-end professional report generation into individual components. |
| Approach: | They propose a benchmarking tool that evaluates 20 real-world professional report generation tasks grounded in multimodal document collections. |
| Outcome: | The proposed model outperforms closed-source models on executive summarization tasks but drops significantly on long-horizon synthesis tasks. |
MoDS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections (2025.naacl-long)
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Nishant Balepur, Alexa Siu, Nedim Lipka, Franck Dernoncourt, Tong Sun, Jordan Lee Boyd-Graber, Puneet Mathur
| Challenge: | Query-focused summarization (QFS) gives an overview of documents to answer a query, ignoring debatable ones. |
| Approach: | They propose a multi-LLM framework that uses a Query-focused summarization approach to create balanced summaries that answer debatable queries. |
| Outcome: | The proposed framework beats SOTA by 38-59% in topic paragraph coverage and balance, based on new citation metrics. |
DocPilot: Copilot for Automating PDF Edit Workflows in Documents (2024.acl-demos)
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| Challenge: | Document workflow copilot system that can understand user intent and execute tasks accordingly to help users streamline their workflows. |
| Approach: | They propose an AI-assisted document workflow copilot system capable of understanding user intent and executing tasks accordingly. |
| Outcome: | The proposed system can understand user intent and execute tasks accordingly to help users streamline their workflows. |