Recommended citation: William Watson and Kazimierz Gofron. (2016). "Development of Computer Vision and Image Processing Libraries at the National Synchrotron Light Source - II." BNL Internship Reports.
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Modeling Color Terminology Across Thousands of Languages

Published in EMNLP-IJCNLP 2019, 2019

Cross-linguistic analysis of color terminology across thousands of languages, linking linguistic and perceptual variation.

Recommended citation: Arya D. McCarthy, Winston Wu, Aaron Mueller, William Watson, and David Yarowsky. (2019). "Modeling Color Terminology Across Thousands of Languages." EMNLP-IJCNLP 2019.
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Customer Experience Focus Can Improve Equity And Credit Performance

Published in S&P Global Special Reports, 2021

Shows that prioritizing customer experience leads to better equity and credit performance outcomes.

Recommended citation: Sheryl Kingstone, Sudeep Kesh, Jeong Choi, Clayton Davis, William Watson, and Sundaram Iyer. (n.d.). "Customer Experience Focus Can Improve Equity And Credit Performance." S&P Global Special Reports.
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Leadership In Turbulent Times: Women CEOs During COVID-19

Published in S&P Global Special Reports (AZBEES Gold Regional Award), 2022

Analyzes leadership strategies of women CEOs during COVID-19 using large-scale earnings call data.

Recommended citation: Daniela Brandazza, Marion Amiot, Katie Darden, William Watson, Gabriel Morin, Rose Marie Burke, Victoria Schumacher, Gaurang Dholakia, Lindsey Hall, Azadeh Nematzadeh, and Nicole Serino. (2022). "Leadership In Turbulent Times: Women CEOs During COVID-19." S&P Global Special Reports.
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BizGraphQA: A Dataset for Image-based Inference over Graph-structured Diagrams from Business Domains

Published in SIGIR 2023, 2023

Provides a dataset enabling question answering over complex business diagrams through graph-structured inference.

Recommended citation: Petr Babkin, William Watson, Zhiqiang Ma, Lucas Cecchi, Natraj Raman, Armineh Nourbakhsh, and Sameena Shah. (2023). "BizGraphQA: A Dataset for Image-based Inference over Graph-structured Diagrams from Business Domains." SIGIR 2023.
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Directed Criteria Citation Recommendation and Ranking Through Link Prediction

Published in ICAIF 2020 (Extended Abstract), 2024

Applies link prediction to recommend and rank financial citations through graph-based methods.

Recommended citation: William Watson and Lawrence Yong. (2020). "Directed Criteria Citation Recommendation and Ranking Through Link Prediction." ICAIF 2020 Extended Abstract.
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BuDDIE: A Business Document Dataset for Multi-task Information Extraction

Published in COLING 2025 FinNLP+FNP+LLMFinLegal (Workshop - Oral), 2024

Introduces BuDDIE, a large-scale dataset of business documents annotated for multiple information extraction tasks.

Recommended citation: Dongsheng Wang, Ran Zmigrod, Mathieu J. Sibue, Yulong Pei, Petr Babkin, Ivan Brugere, Xiaomo Liu, Nacho Navarro, Antony Papadimitriou, William Watson, Zhiqiang Ma, Armineh Nourbakhsh, and Sameena Shah. (2025). "BuDDIE: A Business Document Dataset for Multi-task Information Extraction." COLING 2025 FinNLP+FNP+LLMFinLegal.
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FlowMind: Automatic Workflow Generation with LLMs

Published in ICAIF 2023 (Best Poster Runner-Up), 2024

FlowMind leverages LLMs to automatically generate and refine agentic workflows, outperforming baseline workflow systems.

Recommended citation: Zhen Zeng, William Watson, Nicole Cho, Saba Rahimi, Shayleen Reynolds, Tucker Balch, and Manuela Veloso. (2023). "FlowMind: Automatic Workflow Generation with LLMs." ICAIF 2023.
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Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination, Rewriting, Ranking, and Routing

Published in AAAI 2025 (Oral - Top 4% of Submissions), 2024

HalluciBot predicts hallucination risk before LLM generation, rewriting and routing queries to safer reasoning paths.

Recommended citation: William Watson, Nicole Cho, and Nishan Srishankar. (2025). "Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination, Rewriting, Ranking, and Routing." AAAI 2025.
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Financial Table Extraction in Image Documents

Published in ICAIF 2020 (Oral), 2024

Presents a deep learning pipeline to detect, extract, and transcribe financial tables in images while preserving structure.

Recommended citation: William Watson and Bo Liu. (2020). "Financial Table Extraction in Image Documents." ICAIF 2020.
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HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies

Published in EMNLP 2023, 2024

Introduces HiddenTables and PyQTax for privacy-preserving TableQA; highlights LLM weaknesses on schema alignment and compositional queries.

Recommended citation: William Watson, Nicole Cho, Tucker Balch, and Manuela Veloso. (2023). "HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies." EMNLP 2023.
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FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning

Published in ICAIF 2024, 2024

Designs a modular system combining sub-querying, neural conditioning, and expert swarms to improve financial intelligence tasks.

Recommended citation: Nicole Cho, Nishan Srishankar, Lucas Cecchi, and William Watson. (2024). "FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning." ICAIF 2024.
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LAW: Legal Agentic Workflows for Custody and Fund Services Contracts

Published in COLING 2025 (Oral - Top 15%), 2024

LAW orchestrates modular agents and domain-specific tools to automate complex legal contract workflows with high accuracy.

Recommended citation: William Watson, Nicole Cho, Nishan Srishankar, Zhen Zeng, Lucas Cecchi, Daniel Scott, Suchetha Siddagangappa, Rachneet Kaur, Tucker Balch, and Manuela Veloso. (2025). "LAW: Legal Agentic Workflows for Custody and Fund Services Contracts." COLING 2025.
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MultiQ&A: An Analysis in Measuring Robustness via Automated Crowdsourcing of Question Perturbations and Answers

Published in AAAI 2025 PDLM (Workshop - Oral), 2025

Designs a perturbation analysis to benchmark LLM robustness by sampling perturbed questions and answers.

Recommended citation: Nicole Cho and William Watson. (2025). "MultiQ&A: An Analysis in Measuring Robustness via Automated Crowdsourcing of Question Perturbations and Answers." AAAI 2025 PDLM.
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MultiQ&A: An Analysis in Measuring Robustness via Automated Crowdsourcing of Question Perturbations and Answers

Published in AAAI 2025 PDLM (Workshop - Oral), 2025

Designs a perturbation analysis to benchmark LLM robustness by sampling perturbed questions and answers.

TASER: Table Agents for Schema-guided Extraction and Recommendation

Published in EACL 2026 (Under Review), 2025

Proposes schema-guided table agents for extraction and recommendation in financial and structured data domains.

Recommended citation: Nicole Cho, Kirsty Fielding, William Watson, Sumitra Ganesh, and Manuela Veloso. (2026). "TASER: Table Agents for Schema-guided Extraction and Recommendation." EACL 2026 (Under Review).
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No One Size Fits All: QueryBandits for Hallucination Mitigation

Published in ICLR 2026 (Under Review), 2026

Online per-query rewrites adapt on the fly, prove no one-size-fits-all, and cut hallucinations on closed LLMs.

Recommended citation: Nicole Cho, William Watson, Alec Koppel, Sumitra Ganesh, and Manuela Veloso. (2026). "No One Size Fits All: QueryBandits for Hallucination Mitigation." ICLR 2026 (Under Review).
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