Papers by William Watson
HiddenTables and PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies (2023.emnlp-main)
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| Challenge: | A myriad of different Large Language Models (LLMs) face a common challenge in contextually analyzing table question-answering tasks. |
| Approach: | They propose a cooperative game that is played between the code-generating LLM "Solver" and the "Oracle" it is based on natural language schemas and ensures the security of the underlying data. |
| Outcome: | The proposed game shows that LLMs are ineffective at generalizing and performing on complex queries, handle compositional dependencies, and align natural language to programmatic commands when concrete table schemas are provided. |
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts (2025.coling-industry)
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William Watson, Nicole Cho, Nishan Srishankar, Zhen Zeng, Lucas Cecchi, Daniel Scott, Suchetha Siddagangappa, Rachneet Kaur, Tucker Balch, Manuela Veloso
| Challenge: | Currently, there are limited resources available to build a legal domain-specific Large Language Model (LLM) however, legal contracts are highly varied not only in terms of semantics but also accessibility. |
| Approach: | They propose a Large Language Model (LLM) that integrates multiple specialized agents and text agents to respond to user queries. |
| Outcome: | The proposed model outperforms the baseline model in complex tasks such as calculating a contract’s termination date by 92.9% points. |
Modeling Color Terminology Across Thousands of Languages (D19-1)
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| Challenge: | Existing studies on what constitutes a "basic" color term and its acquisition sequence are flawed . a pan-lingual approach may reveal general color trends more reliably than smaller datasets. |
| Approach: | They propose to operationalize and critique the Berlin and Kay color term hypotheses . they use 14 empirically-grounded computational linguistic metrics to analyze cross-linguistic data . |
| Outcome: | The proposed measures correlate strongly with the Berlin and Kay color term partition and their hypothesized universal acquisition sequence. |
What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are often treated as defects of the model or its decoding strategy. |
| Approach: | They construct a 22-dimension query feature vector covering clause complexity, lexical rarity, anaphora, negation, answerability, and intention grounding. |
| Outcome: | The proposed model covers clause complexity, lexical rarity, anaphora, negation, answerability, and intention grounding, all known to affect human comprehension. |
TASER: Table Agents for Schema-guided Extraction and Recommendation (2026.eacl-industry)
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| Challenge: | Real-world financial filings report critical information about an entity’s investment holdings, but they are often buried in messy, multi-page, fragmented tables that are difficult to parse. |
| Approach: | They propose to train a system that converts highly unstructured, multi-page, heterogeneous tables into normalized, schema-conforming outputs. |
| Outcome: | The proposed system outperforms vision-based table detection models by 10.1% and can generate more useful recommendations by 10%. |