Papers by Mahsa Shamsabadi
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology (2024.findings-eacl)
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| Challenge: | Scholarly communication in the digital age is facing significant challenges due to the overwhelming volume of publications. |
| Approach: | They propose to use Wikipedia infoboxes and structured Amazon product descriptions to create structured scholarly contribution summaries using text generation capabilities of LLMs. |
| Outcome: | The proposed model can be applied to complex IE tasks within terse domains like Science with 1000x fewer parameters than the state-of-the-art GPT-davinci. |