Papers by Angelina Parfenova
Automating Qualitative Data Analysis with Large Language Models (2024.acl-srw)
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| Challenge: | Existing methods for qualitative data analysis are far from resembling a human's analysis outcome. |
| Approach: | They propose a method based on Large Language Models to tackle automated coding and make it as close as possible to the results of human researchers. |
| Outcome: | The proposed method is based on large language models and can be as close as possible to the results of human researchers. |
Measuring What Matters: Evaluating Ensemble LLMs with Label Refinement in Inductive Coding (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are prone to inconsistencies and individual biases, limiting their reliability. |
| Approach: | They propose a framework that combines ensemble methods with code refinement methodology to address these challenges. |
| Outcome: | The proposed framework outperforms large language models and LLMs with a low-rank averaging and a moderator-based mechanism to simulate human consensus. |
Text Annotation via Inductive Coding: Comparing Human Experts to LLMs in Qualitative Data Analysis (2025.findings-naacl)
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| Challenge: | Qualitative data analysis (QDA) is an important research method across fields such as marketing, media studies, social science, psychology, medical research, and others. |
| Approach: | They evaluate the performance of open-source LLMs by comparing them to human experts. |
| Outcome: | The proposed method is based on inductive coding using large language models. |