Papers by Nupoor Gandhi
Beyond Text: Characterizing Domain Expert Needs in Document Research (2025.findings-acl)
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| Challenge: | Document research is a key part of almost all knowledge work, but are text-based NLP systems able to model these tasks as experts conceptualize and perform them? |
| Approach: | They interview 16 domain experts to understand their processes of document research . they find that processes are idiosyncratic, iterative, and rely heavily on social context . |
| Outcome: | The findings show that document research processes are idiosyncratic, iterative, and rely heavily on the social context of a document in addition to its content. |
Decomposing Unitization and Typing for Efficient and Consistent Span-Bound Concept Annotation (2026.findings-acl)
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| Challenge: | Substantial resources are typically spent on unitizing, the task of identifying precise span boundaries for entity mentions. |
| Approach: | They propose a method that focuses manual efforts on typed position annotations instead of full concept annotation. |
| Outcome: | The proposed procedure reduces the cost of concept annotations by focusing on typed positions instead of full concept annotation. |
Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains (2024.findings-emnlp)
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| Challenge: | a lack of anonymization of sensitive text data hinders development of NLP tools . poorly anonymized sensitive data cannot be easily shared with annotators or external researchers . |
| Approach: | They propose to use synthetic data to generate differentially private language models in place of real data to facilitate NLP development without compromising privacy. |
| Outcome: | The proposed model can be used to train public models without compromising privacy. |
Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution (2023.acl-long)
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| Challenge: | Recent results show that annotating mentions is twice as fast as annotation of full coreference chains. |
| Approach: | They propose a method for efficiently adapting coreference models using only mentions in the target domain without increasing annotator time. |
| Outcome: | The proposed method improves average F1 without increasing annotator time. |
SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains (2025.emnlp-demos)
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Krithika Ramesh, Daniel Smolyak, Zihao Zhao, Nupoor Gandhi, Ritu Agarwal, Margrét V. Bjarnadóttir, Anjalie Field
| Challenge: | SynthTextEval is a toolkit for conducting comprehensive evaluations of synthetic text. |
| Approach: | They propose a toolkit for conducting comprehensive evaluations of synthetic text using large language models. |
| Outcome: | The proposed toolkit can be run over any dataset, but it is aimed at two high-stakes domains: healthcare and law. |