ConfliBERT: A Language Model for Political Conflict

• URL: http://arxiv.org/abs/2412.15060v1
• Date: Thu, 19 Dec 2024 17:08:11 GMT
• Title: ConfliBERT: A Language Model for Political Conflict
• Authors: Patrick T. Brandt, Sultan Alsarra, Vito J. D`Orazio, Dagmar Heintze, Latifur Khan, Shreyas Meher, Javier Osorio, Marcus Sianan,
• Abstract summary: Conflict scholars have used rule-based approaches to extract information about political violence from news reports and texts. We review our recent ConfliBERT language model to process political and violence related texts. When fine-tuned, results show that ConfliBERT has superior performance in accuracy, precision and recall over other large language models.
• Score: 7.031352908995972
• License:
• Abstract: 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).

Related papers

• Narrative Analysis of True Crime Podcasts With Knowledge Graph-Augmented Large Language Models [8.78598447041169]
  Large language models (LLMs) still struggle with complex narrative arcs as well as narratives containing conflicting information. Recent work indicates LLMs augmented with external knowledge bases can improve the accuracy and interpretability of the resulting models. In this work, we analyze the effectiveness of applying knowledge graphs (KGs) in understanding true-crime podcast data.
  arXiv  Detail & Related papers  (2024-11-01T21:49:00Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models? [49.83570853386928]
  Humans convey and pass down commonsense implicitly through stories. This paper investigates the inherent commonsense ability of large language models (LLMs) expressed through stories.
  arXiv  Detail & Related papers  (2024-02-22T07:55:26Z)
• Into the crossfire: evaluating the use of a language model to crowdsource gun violence reports [0.21485350418225244]
  We propose a fine-tuned BERT-based model trained on Twitter texts to distinguish gun violence reports from ordinary Portuguese texts. We study and interview Brazilian analysts who continuously fact-check social media texts to identify new gun violence events.
  arXiv  Detail & Related papers  (2024-01-16T14:40:54Z)
• Mavericks at BLP-2023 Task 1: Ensemble-based Approach Using Language Models for Violence Inciting Text Detection [0.0]
  Social media has accelerated the propagation of hate and violence-inciting speech in society. The problem of detecting violence-inciting texts is further exacerbated in low-resource settings due to sparse research and less data. This paper presents our work for the Violence Inciting Text Detection shared task in the First Workshop on Bangla Language Processing.
  arXiv  Detail & Related papers  (2023-11-30T18:23:38Z)
• Fine-tuning Language Models for Factuality [96.5203774943198]
  Large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations' In this work, we fine-tune language models to be more factual, without human labeling.
  arXiv  Detail & Related papers  (2023-11-14T18:59:15Z)
• Skywork: A More Open Bilingual Foundation Model [55.927396986873816]
  We present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. We show that our model not only excels on popular benchmarks, but also achieves emphstate of the art performance in Chinese language modeling on diverse domains.
  arXiv  Detail & Related papers  (2023-10-30T08:31:47Z)
• Using Word Embeddings to Analyze Protests News [2.024222101808971]
  Two well performing models have been chosen in order to replace the existing word embeddings word2vec and FastTest with ELMo and DistilBERT. Unlike bag of words or earlier vector approaches, ELMo and DistilBERT represent words as a sequence of vectors by capturing the meaning based on contextual information in the text.
  arXiv  Detail & Related papers  (2022-03-11T12:25:59Z)
• From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French [57.886210204774834]
  We present our efforts to develop NLP tools for Early Modern French (historical French from the 16$textth$ to the 18$textth$ centuries). We present the $textFreEM_textmax$ corpus of Early Modern French and D'AlemBERT, a RoBERTa-based language model trained on $textFreEM_textmax$.
  arXiv  Detail & Related papers  (2022-02-18T22:17:22Z)
• Neural Models for Offensive Language Detection [0.0]
  Offensive language detection is an ever-growing natural language processing (NLP) application. We believe contributing to improving and comparing different machine learning models to fight such harmful contents is an important and challenging goal for this thesis.
  arXiv  Detail & Related papers  (2021-05-30T13:02:45Z)
• MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models [98.53511229517463]
  We propose a novel framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base. Our framework consists of a keyword predictor, a knowledge retriever, a contextual knowledge ranker, and a conditional text generator. The empirical results show that our model generates more fluent, consistent, and coherent stories with less repetition and higher diversity compared to prior work on the ROC story dataset.
  arXiv  Detail & Related papers  (2020-10-02T08:07:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.