Papers by Preethi Raghavan
emrQA: A Large Corpus for Question Answering on Electronic Medical Records (D18-1)
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| Challenge: | Existing annotations for other NLP tasks are used to generate domain-specific large-scale question answering (QA) datasets. |
| Approach: | They propose to re-purpose existing annotations for other NLP tasks by generating a large-scale question answering corpus using 1 million questions-logical form and 400,000+ question-answer evidence pairs. |
| Outcome: | The proposed model can be trained to learn domain-specific large-scale question answering (QA) datasets. |
BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra (2024.findings-acl)
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| Challenge: | Existing hybrid question answering systems use a "prompt-and-pray" paradigm . context size limitations limit ability of many transformer-based LLMs to fit into a given prompt . |
| Approach: | They propose a superset of SQLite to act as a unified dialect for orchestrating reasoning across unstructured and structured data. |
| Outcome: | The proposed framework scales to massive datasets and improves performance while using 35% fewer tokens. |
Self-training Strategies for Sentiment Analysis: An Empirical Study (2024.findings-eacl)
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| Challenge: | Sentiment analysis is a crucial task in natural language processing. |
| Approach: | They propose to leverage a small amount of labeled and unlabeled data to train models with self-training. |
| Outcome: | The proposed method improves the performance of small language models in several few-shot settings while reducing the cost of annotations. |