Papers by Jane Zhang

3 papers
P-QuASAR: A Unified Probabilistic Framework for Holistic Patent Quality Assessment and Refinement (2026.findings-acl)

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Challenge: Existing methods for assessing patent quality rely on modular pipelines or generic detectors, resulting in fragmented decisions and limited integration across quality dimensions.
Approach: They propose a probabilistic framework that represents patent specifications as Quality Graphs.
Outcome: The proposed framework outperforms existing methods on 500 patents against seven baselines.
Extrapolating to Unknown Opinions Using LLMs (2025.coling-main)

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Challenge: ice cream flavors and climate change are among the topics people hold on various topics.
Approach: They propose to use a large language model to extrapolate from stances to unknown opinions by prompting and fine-tuning data to improve their ability to extrapole from known to unknown stance.
Outcome: The proposed model can extrapolate from opinions on known topics to unknown ones and generate reasoning behind extrapolation.
ROBBIE: Robust Bias Evaluation of Large Generative Language Models (2023.emnlp-main)

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Challenge: generative large language models (LLMs) are becoming more performant and prevalent . we need tools to measure and improve their fairness, authors say .
Approach: They propose to compare 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative large language models.
Outcome: The proposed model can be tested on more datasets to better characterize and mitigate biases . the study compared 6 prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative large language models.

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