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Prompt

I feel like the best way to execute on this use case has to be something like an LLM that is trained on this keyword array but which is intelligent enough to think a bit beyond it. Can you think of any LLM that could be used for this purpose? Are there any taht have been developed with live news monitoring in mind?

Output

For a more intelligent system capable of interpreting security events beyond keyword matching, a Large Language Model (LLM) is indeed a suitable choice. While LLMs are primarily used for natural language processing (NLP), they can be adapted to the task of news monitoring with contextual understanding beyond keyword arrays. Let’s break down the options available and how you could use LLMs for this use case.

1. Hugging Face LLMs:

Hugging Face is one of the best platforms for exploring and deploying LLMs trained for specific tasks, and there are several models that can be adapted for live news monitoring:

  • DistilBERT and RoBERTa: These models are excellent for text classification and entity recognition. You could fine-tune these models on a dataset of news articles labeled by severity (i.e., severe, moderate, low) based on your keyword array. With transfer learning, the model could be trained to understand security-related contexts in Israel and Jerusalem.
  • LLMs with text similarity capabilities: Models like Sentence-BERT or OpenAI’s GPT models can help you measure the similarity between incoming news articles and your keyword array. This will allow the model to "think beyond" exact keyword matching by understanding the semantic meaning behind news events, even when keywords are slightly different.

Hugging Face hosts multiple models pre-trained for various NLP tasks that you can fine-tune or use directly for news classification and monitoring.

2. OpenAI GPT-4:

  • GPT-4 (or GPT-4 Turbo) can be fine-tuned or used with few-shot learning for contextual news monitoring. While OpenAI models aren’t live-trainable in real time, you can build a system where the LLM regularly queries incoming news data, processes it, and classifies the news based on how closely it relates to your security keywords.
  • Customization with prompt engineering: GPT-4’s prompt engineering capabilities can make it intelligent enough to recognize and classify unseen but similar events. You can create a structured prompt that guides the model to recognize threats even when not explicitly listed in your keyword array.
  • Use-case specific example: 
    "You are a security monitoring assistant tasked with identifying security events in Jerusalem and Israel. 
Prioritize terrorist attacks, rocket sirens, bombings, shootings, and civil unrest. 
If a news article contains an event that matches or is related to the following keywords, classify it as 'Most Severe' or 'Very Severe': [list of keywords]. 
Use your judgment to assess how closely related news is to the terms provided."

3. Real-Time News Monitoring Solutions:

Some LLMs and AI systems have been explicitly designed for monitoring live events, including news monitoring, though not always specific to security or Israel. However, these platforms can be adapted for your use case:

  • GDELT (Global Data on Events, Location, and Tone): GDELT is a real-time system designed to monitor global news and is one of the most comprehensive open-source platforms for analyzing news events. While not an LLM, it can work in conjunction with LLMs to give you structured data (like news reports and metadata) that you can then process through your trained LLM for threat detection.
  • AWS Comprehend: AWS Comprehend provides NLP models that are designed for text analysis. You can fine-tune it for specific needs, such as understanding the severity of security events, by training it on a dataset labeled by your severity levels. AWS Comprehend can help classify and extract key phrases and entities from real-time news streams.

4. Training Custom LLM with Domain-Specific Data:

If you're willing to go a step further, you can create a custom-trained LLM focused on security events in Israel. By combining data from sources like the GDELT Project, Israel-specific news datasets, and historical security data, you can fine-tune an open-source LLM like BERT, T5, or GPT-2 to predict and classify security events.

Here’s the process:

  1. Data Collection: Gather news articles, blogs, and social media posts related to security events in Israel, categorized into severity levels.
  2. Fine-Tuning: Use this dataset to fine-tune a model from Hugging Face (e.g., BERT, GPT-2) to classify and understand the security context.
  3. Integration with Live News Feeds: Connect this model to live data streams (e.g., from a news API or GDELT) for real-time monitoring.

5. LLMs Developed for Real-Time Text Understanding:

There are a few examples of LLMs developed with real-time text understanding in mind:

  • BloombergGPT: This LLM is designed for financial news monitoring but can be adapted to analyze and understand security-related news in real-time.
  • Reuters NLP API: Reuters offers real-time text analysis tools that could be coupled with LLMs for context-specific classification.

6. Rasa or Other Open-Source NLP Frameworks:

  • Rasa is an open-source framework that you can train for text classification and NLP tasks, including extracting intent from news headlines. By training Rasa with your dataset, you can use it as a lightweight LLM to classify security news events in real time.

Suggested Workflow:

  1. Collect News: Use a news API or GDELT for Israel-specific news.
  2. Preprocess with Keyword Array: Run an initial filter using your keyword array for faster classification.
  3. LLM Processing: Feed filtered news into an LLM like GPT-4, which will analyze the event's severity and context beyond the keywords.
  4. Alert Trigger: If the LLM classifies the event as a high-priority security event (based on semantic understanding), trigger the appropriate alert.
  5. Feedback Loop: As new events are detected, continue to retrain or fine-tune the model to improve accuracy over time.

Conclusion:

For real-time security news monitoring, a combination of fine-tuned LLMs (like Hugging Face’s BERT or GPT-4) with real-time data feeds (e.g., from GDELT or Google News API) would give you the best of both worlds. You get real-time updates, an intelligent system that understands the nuances of security events, and the ability to "think beyond" simple keyword matching by leveraging semantic understanding.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.