ConflLlama
Domain-specific adaptation of large language models for conflict event classification
Bibliographic Data
| ID | 6448528 |
|---|---|
| Authors | Shreyas Meher (0000-0002-9656-4374, The University of Texas at Dallas), Patrick T Brandt (0000-0002-7261-7056, The University of Texas at Dallas) |
| Year | 2025 |
| Volume | 12 |
| Issue | 3 |
| Publication date | 2025-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Research & Politics (JOURNAL) |
| Journal identifiers | ISSN: 2053-1680 • E-ISSN: 2053-1680 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/20531680251356282 |
| OpenAlex | W4412106274 |
| Language | EN |
| Citations received | 1 |
| References cited | 12 |
We present ConflLlama, demonstrating how efficient fine-tuning of large language models can advance automated classification tasks in political science research. While classification of political events has traditionally relied on manual coding or rigid rule-based systems, modern language models offer the potential for more nuanced, context-aware analysis. However, deploying these models requires overcoming significant technical and resource barriers. We demonstrate how to adapt open-source language models to specialized political science tasks, using conflict event classification as our proof of concept. Through quantization and efficient fine-tuning techniques, we show state-of-the-art performance while minimizing computational requirements. Our approach achieves a macro-averaged AUC of 0.791 and a weighted F1-score of 0.753, representing a 37.6% improvement over the base model, with accuracy gains of up to 1463% in challenging classifications. We offer a roadmap for political scientists to adapt these methods to their own research domains, democratizing access to advanced NLP capabilities across the discipline. This work bridges the gap between cutting-edge AI developments and practical political science research needs, enabling broader adoption of these powerful analytical tools
Adaptation (eye · Domain (mathematical analysis · Domain adaptation · Event (particle physics · Linguistics · Natural language processing · Physics · Computer Science · Mathematics · Natural Language Processing Techniques · Network Security and Intrusion Detection · Psychology · Topic Modeling · Artificial Intelligence
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |