Papers by Karthik Venkat Ramanan
DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance (2023.findings-acl)
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| Challenge: | Existing Dynamic topic models are either fully supervised, requiring expensive human annotations, or fully unsupervised, producing topic evolutions that often do not cater to a user’s needs. |
| Approach: | They propose to use a framework that ensembles semantic similarity, category indicative, and time indicative scores to produce informative topic evolutions. |
| Outcome: | The proposed framework can be used to discover topic evolutions from temporal corpora that align with user-provided category names and uniquely capture topics at each time step. |
Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges (2024.acl-srw)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive zero-shot performance on a wide range of NLP tasks. |
| Approach: | They propose to use large language models to augment extractive reading comprehension datasets by fine-tuning their annotations and comparing their performance to human annotators. |
| Outcome: | The proposed model can be used to augment extractive reading comprehension datasets. |