Papers by Tucker Balch
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. |
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations (2025.acl-long)
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| Challenge: | State-of-the-art multimodal web agents can perform many web tasks by processing user instructions and interacting with graphical user interfaces (GUIs). |
| Approach: | They propose to build multimodal web agents for few-shot adaptability using human demonstrations to improve their generalization and adaptability. |
| Outcome: | The proposed framework enables both proprietary and open-weights multimodal web agents to adapt to new websites and domains using few human demonstrations. |
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark (2024.emnlp-main)
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Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, Manuela Veloso, Svitlana Vyetrenko
| Challenge: | Large Language Models (LLMs) are a critical tool for time series analysis and reporting in many fields, including healthcare, finance, climate, and many more. |
| Approach: | They propose a framework for rigorously evaluating the capabilities of Large Language Models (LLMs) on time series understanding, encompassing both univariate and multivariate forms. |
| Outcome: | The proposed framework delineates various characteristics inherent in time series data. |
LETS-C: Leveraging Text Embedding for Time Series Classification (2025.acl-long)
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| Challenge: | Recent advances in language modeling have shown promising results when applied to time series data. |
| Approach: | They propose a method to fine-tune large language models for time series classification tasks using text embedding models and a simple classification head. |
| Outcome: | The proposed model outperforms the current SOTA model on a time series classification benchmark and uses only 14.5% of the trainable parameters. |