Summary of Comparative Performance Of Advanced Nlp Models and Llms in Multilingual Geo-entity Detection, by Kalin Kopanov
Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection
by Kalin Kopanov
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper evaluates the performance of leading Natural Language Processing (NLP) models and Large Language Models (LLMs) for multilingual geo-entity detection. It compares SpaCy, XLM-RoBERTa, mLUKE, GeoLM, OpenAI’s GPT 3.5, and GPT 4 on datasets from English, Russian, and Arabic Telegram channels. The evaluation metrics include accuracy, precision, recall, and F1 scores. The analysis highlights each model’s strengths and weaknesses, emphasizing the complexities of achieving precise geo-entity identification across diverse linguistic landscapes. This research aims to inform the development of more advanced and inclusive NLP tools for geospatial analysis and global security applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well different computer programs can find geographic references in texts written in multiple languages. They compared six models: SpaCy, XLM-RoBERTa, mLUKE, GeoLM, OpenAI’s GPT 3.5, and GPT 4. They tested these models on text messages from English, Russian, and Arabic. The results show which models are best at finding geographic references in each language. This research helps us create better computer programs for understanding texts and keeping the world safe. |
Keywords
» Artificial intelligence » Gpt » Natural language processing » Nlp » Precision » Recall
