ConfliBERT: A Language Model for Political Conflict
This provides a more efficient and accurate tool for conflict scholars analyzing political violence from news reports and datasets, though it is incremental as it builds on existing language model approaches.
The paper tackles the problem of extracting actor and action classifications from political conflict texts, reporting that ConfliBERT achieves superior accuracy, precision, recall, and is hundreds of times faster than generalist large language models like Gemma 2, Llama 3.1, and Qwen 2.5 in relevant domains.
Conflict scholars have used rule-based approaches to extract information about political violence from news reports and texts. Recent Natural Language Processing developments move beyond rigid rule-based approaches. We review our recent ConfliBERT language model (Hu et al. 2022) to process political and violence related texts. The model can be used to extract actor and action classifications from texts about political conflict. When fine-tuned, results show that ConfliBERT has superior performance in accuracy, precision and recall over other large language models (LLM) like Google's Gemma 2 (9B), Meta's Llama 3.1 (7B), and Alibaba's Qwen 2.5 (14B) within its relevant domains. It is also hundreds of times faster than these more generalist LLMs. These results are illustrated using texts from the BBC, re3d, and the Global Terrorism Dataset (GTD).