Papers by Defne Circi
Extracting Polymer Nanocomposite Samples from Full-Length Documents (2024.findings-acl)
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| Challenge: | Using large language models (LLMs) to extract sample lists of polymer nanocomposites (PNCs) from full-length materials science research papers is challenging due to the complexity of the data. |
| Approach: | They propose a benchmark and evaluation technique for extracting sample lists of polymer nanocomposites from full-length materials science research papers. |
| Outcome: | The proposed method improves the performance of LLMs and incorporates self-consistency to improve the performance. |
Evaluating Morphological Compositional Generalization in Large Language Models (2025.naacl-long)
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Mete Ismayilzada, Defne Circi, Jonne Sälevä, Hale Sirin, Abdullatif Köksal, Bhuwan Dhingra, Antoine Bosselut, Duygu Ataman, Lonneke Van Der Plas
| Challenge: | Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. |
| Approach: | They define morphemes as compositional primitives and design a suite of generative and discriminative tasks to assess morphological productivity and systematicity. |
| Outcome: | The proposed models can identify individual morphological combinations better than chance, but their performance lacks systematicity, leading to significant accuracy gaps compared to humans. |