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News Topic Classifier

About this AI

Summary

The AI News Article Classifier is designed to efficiently categorize news articles by topic using large language models (LLMs). By offering an accessible API, it allows users to submit news articles in plain text and receive the classified topic in return. This innovation aims to enhance content management and personalize user experiences by facilitating accurate and efficient categorization of news content. Through prompt engineering, robust validation, and testing frameworks, the AI ensures high classification accuracy and reliability, meeting the demands of modern digital news environments.

Major Use Cases

<b>Editorial Assistance</b>: Automate sorting of incoming news articles.
<b>Reader Personalization</b>: Provide curated article recommendations to users.
<b>Content Management</b>: Efficiently organize digital news archives by topic.
<b>Trend Analysis</b>: Identify popular topics and emerging trends.
<b>Media Monitoring</b>: Enable real-time topic classification of news streams.

Milestone

PRD Completion: We have drafted a comprehensive Product Requirements Document, outlining the solution's objective to leverage LLMs for classifying news articles by topic.
AI Architecture Development: We have built the AI architecture leveraging a two-step classification process: "Topic Extraction" and "Topic Refinement" using the GPT-4 model.
Testing and Validation: We have conducted quick tests confirming the AI's high accuracy in classifying topics such as "Science," "Politics," and "Entertainment," aligned to predefined categories.
Integration as API: We have compiled the AI as an accessible API, allowing efficient integration into other services needing news article categorization.

AI Architecture & Logic Plans

AI Plans

AI Plans ListClick to see details
TwoStepNewsTopicClassifier

API INPUT KEYS
news_articleText
STEPS
Topic Extraction
Model
openai / gpt-4o
Prompt
### Prompt for LLM - Topic Extraction You are an AI analyst tasked with extracting potential topic candidates from a provided news article. Read the article carefully to identify primary themes. ### Context Understanding Analyze the content of the following news article to comprehend the main ideas and themes presented within: ``` {{news_article}}Value of the API input "news_article" is inserted ``` ### Key Phrase Identification Identify and note down recurring subjects and key phrases present in the text. Focus on capturing the broader aspects of the document to ensure a comprehensive topical extraction. ### Few-shot Learning Example Example Article: "A breakthrough in renewable energy has potential to revolutionize the way we harness solar power." Extracted Topics: [{"topic": "Renewable Energy"}, {"topic": "Solar Power"}] ### Expected Output Provide a list of potential topics derived from the news article in the following format: ``` [{"topic": "Topic1"}, {"topic": "Topic2"}, ...] ``` Ensure that the output does not include unnecessary prefixes or explanations. ### Output Response:
Topic Refinement
Model
openai / gpt-4o
Prompt
## Role: Topic Evaluator You are tasked with evaluating and refining initial topic candidates to select the most relevant and accurate topic for a news article. ## Input Details: List of Initial Topic Candidates: {{SV42UdKETTWe7R-c4eldTQ}}Value of the result from the step "Topic Extraction" is inserted ## Predefined Topics: - Technology - Politics - Health - Entertainment - Sports - Business - Environment - Science ## Task Instructions: Evaluate the initial topic candidates by comparing them to the predefined topics. Select the single most contextually relevant topic that aligns with the content of the news article. Ensure that your selection is based on semantic similarity and contextual relevance. ## Output Respond with a JSON object indicating the selected topic, formatted as: `{"topic": "Selected Topic"}` Do not include any unnecessary prefixes in your output.
API OUTPUT KEYS
topicTopic RefinementText

Here are several examples of the input and output of this model.

Quick Test

Input
AI Synthesized
news_article
Run
2 steps1,807 ms
1. Topic ExtractionPrompt Bookopenai/gpt-4o1,299 ms
Prompt
### Prompt for LLM - Topic Extraction You are an AI analyst tasked with extracting potential topic candidates from a provided news article. Read the article carefully to identify primary themes. ### Context Understanding Analyze the content of the following news article to comprehend the main ideas and themes presented within: ``` The new blockbuster movie has taken the global box office by storm, becoming one of the highest-grossing films of the year. Critics laud the film's stunning visual effects and compelling storytelling, while fan communities around the world continue to celebrate the film's cultural impact. ``` ### Key Phrase Identification Identify and note down recurring subjects and key phrases present in the text. Focus on capturing the broader aspects of the document to ensure a comprehensive topical extraction. ### Few-shot Learning Example Example Article: "A breakthrough in renewable energy has potential to revolutionize the way we harness solar power." Extracted Topics: [{"topic": "Renewable Energy"}, {"topic": "Solar Power"}] ### Expected Output Provide a list of potential topics derived from the news article in the following format: ``` [{"topic": "Topic1"}, {"topic": "Topic2"}, ...] ``` Ensure that the output does not include unnecessary prefixes or explanations. ### Output Response:
Compl.
``` [{"topic": "Blockbuster Movie"}, {"topic": "Global Box Office"}, {"topic": "Visual Effects"}, {"topic": "Storytelling"}, {"topic": "Cultural Impact"}] ```
2. Topic RefinementPrompt Bookopenai/gpt-4o505 ms
Prompt
## Role: Topic Evaluator You are tasked with evaluating and refining initial topic candidates to select the most relevant and accurate topic for a news article. ## Input Details: List of Initial Topic Candidates: ``` [{"topic": "Blockbuster Movie"}, {"topic": "Global Box Office"}, {"topic": "Visual Effects"}, {"topic": "Storytelling"}, {"topic": "Cultural Impact"}] ``` ## Predefined Topics: - Technology - Politics - Health - Entertainment - Sports - Business - Environment - Science ## Task Instructions: Evaluate the initial topic candidates by comparing them to the predefined topics. Select the single most contextually relevant topic that aligns with the content of the news article. Ensure that your selection is based on semantic similarity and contextual relevance. ## Output Respond with a JSON object indicating the selected topic, formatted as: `{"topic": "Selected Topic"}` Do not include any unnecessary prefixes in your output.
Compl.
{"topic": "Entertainment"}
Output
from your model in draft
topic
{"topic": "Entertainment"}
Quick Evaluation by LLM Judges
Metric
Accuracy of News Article Topic Classification
Score
PERFECT
Reason
The model correctly identifies the topic as "Entertainment" which aligns with the news article content about a new movie's success. The response adheres to the specified JSON output format (a JSON object with a "topic" key). The model demonstrates high accuracy in topic classification for this specific article. Expand
Metric
Prompt Cohesion
Score
PERFECT
Reason
The prompt effectively guides the LLM to classify the topic as "Entertainment." The model response correctly identifies the topic as "Entertainment" based on the news article content about a blockbuster movie. The prompt successfully aligns with the objective of accurate topic categorization. Expand
Metric
Relevance Scoring of Topic Classification
Score
PERFECT
Reason
The model response correctly identifies "Entertainment" as the topic, which accurately reflects the content of the news article about a new blockbuster movie. The response directly addresses the user's request by providing a relevant topic, aligning with the expected use cases of Editorial Assistance, Reader Personalization, Content Management, Trend Analysis, and Media Monitoring. The response adheres to the API output format requirement of a JSON object containing the "topic" key. Expand

Evaluation Results

Evaluation Report

Evaluation Report ListClick to see details
AI Evaluation Report at 2025-03-03 02:47

Introduction

Evaluation target plan

[TwoStepNewsTopicClassifier](/genflows/HrWeqqMIRRaRFnT7SqLltg/develop/b8Y36lLLRRWCOpwKUMyocA)

Datasets to test this AI model

We've prepared 50 cases from 5 major use cases, generated by LLM Dataset Synthesizer, such as:
Editorial Assistance: Automate sorting of incoming news articles
Reader Personalization: Provide curated article recommendations to users
Content Management: Efficiently organize digital news archives by topic
Trend Analysis: Identify popular topics and emerging trends
Media Monitoring: Enable real-time topic classification of news streams

LLM Judge

We've simulated this AI from the prepared test datasets and analyzed the response by LLM Judges.
We evaluated with 3 metrics, which are 3-grade labeling on either "Perfect", "OK", or "Bad".
The LLM Judges used are as follows:
<!-- Briefing of LLM Judge (or metrics) definitions should be here -->

Evaluation Results

Performance

The AI's performance was evaluated across three key metrics: Accuracy of News Article Topic Classification, Prompt Cohesion, and Relevance Scoring of Topic Classification.
Percentage of Each Evaluation Metric
Accuracy of News Article Topic Classification: The AI achieved a 90.6% success rate, with a notable 9.4% in the "Bad" category, suggesting room for improvement in classification precision.
Prompt Cohesion: The model performed well with a high 92.5% in the "Perfect" category. However, the 3.8% "Bad" rating indicates occasional lack of consistency in prompt alignment.
Relevance Scoring: Similarly, 92.5% of responses were rated "Perfect", but with a 7.5% falling into the "Bad" category, suggesting some topics may not always be perfectly relevant.
Average Score Comparison by Use Cases
Across Use Cases, the performance varied, highlighting strengths and weaknesses:
Content Management, Reader Personalization, and Media Monitoring: Achieved a perfect score of 2.0, indicating robust and consistent classification.
Trend Analysis and Editorial Assistance: Received scores of 1.7-1.8, signifying there may be some challenges in these scenarios—potentially due to nuanced topic trends and real-time article sorting.
Takeaways:
For improving Accuracy, focus on refining the model to handle edge cases and ambiguous topics better.
To enhance Prompt Cohesion, consider revising and optimizing prompt structures.
Relevance Scoring could benefit from further training on diverse datasets, improving the AI's ability to understand and match topical relevance across varying contexts.
🌟 Recommendation: Prioritize enhancements in Trend Analysis and Editorial Assistance to mitigate weaknesses and elevate overall AI performance.

Potential Hallucinations & Common Error Patterns

Incorrect Topic Classification: A key error pattern observed involves inaccurate topic categorization in certain contexts. For example, when the news article titled "The painting, previously thought to be lost, was found in an unexpected location, igniting discussions about its mysterious past and its significance in art history." was misclassified by the AI as {"topic": "Entertainment"}. This failure shows a lack of distinguishing between art history and entertainment.
Contextual Misalignment: The AI sometimes fails to capture the contextual nuances of the news article, leading to misclassification. Instances like "A groundbreaking ceremony was held for a new building, but midway the event, discussions shifted to an unrelated topic of economic policies.", which was labeled as {"topic": "Business"}, demonstrate its struggle to properly weigh multiple subjects within the same article.
Overlap in Topics: There are cases where the AI demonstrates confusion with overlapping topics, such as in "In unrelated news, a politician and a tech leader were seen discussing projects, sparking speculation about future collaborations." Here, the need for a broader classification beyond {"topic": "Politics"} is apparent, recognizing the tech-business intersection.
These errors highlight the AI's difficulty in nuanced distinctions between closely related subjects and complex multi-topic articles, an area requiring refined prompt engineering and improved topic differentiation strategies.

Conclusions

Is this model production ready?

Almost ready, the model shows strong performance across most scenarios, with headlines such as an 90.6% accuracy rate in classifying news articles by topic and 92.5% effectiveness in both prompt cohesion and relevance scoring. However, challenges remain, with scores in the "Bad" category ranging from 3.8% to 9.4%. Given there are still areas for improvement, particularly in handling nuanced and edge-case topics, it is recommended to consider it almost production-ready with continued monitoring and refinement for unexpected inputs.
It is crucial to remind that though these metrics are promising, human oversight remains necessary. Continuous log reviews should be conducted to ensure the model performs robustly across varying inputs beyond the controlled evaluation phase.

Future Improvements

Enhance Contextual Understanding: Invest resources in developing enhanced contextual understanding capabilities to avoid misclassification errors due to overlapping topics or multi-subject articles. Implementing a more sophisticated context-discerning mechanism could provide a finer-grained topic distinction and potentially reduce errors in these areas.
Refine Prompt Engineering: Further refine and optimize prompt engineering strategies. This involves creating a dataset that includes more edge cases and ambiguous topics to train the AI on, focusing on the areas of Trend Analysis and Editorial Assistance, where performance scores indicated room for improvement. By iterating over a broader set of scenarios, the model can deliver higher accuracy and prompt cohesion across all use cases.

Integration

How this model is served

This AI model for classifying news articles by topic is fully deployed and accessible via a dedicated API endpoint hosted on Teammately: https://tmmt.ly/:id.

Integration Example

For use case scenarios where automated content management is essential, you can integrate this AI solution into a content management system to automatically categorize news articles. Below are integration examples for two different environments:
1.Python Integration for Content Management Systems
python
import requests
import json

def classify_article(article_text):
    url = "https://tmmt.ly/:id"
    headers = {
        "Authorization": "Bearer YOUR_TEAMMATELY_API_KEY",
        "Content-Type": "application/json"
    }
    payload = {"input": article_text}

    response = requests.post(url, headers=headers, data=json.dumps(payload))
    
    if response.status_code == 200:
        topic = response.json().get("topic")
        return topic
    else:
        raise Exception(f"Request failed with status code {response.status_code}")

# Example usage
article_text = "This is a sample news article discussing the latest advances in artificial intelligence."
topic = classify_article(article_text)
print(f"The article is about: {topic}")
1.JavaScript Integration for Web-based Editorial Systems
javascript
async function classifyArticle(articleText) {
    const url = "https://tmmt.ly/:id";
    const headers = {
        "Authorization": "Bearer YOUR_TEAMMATELY_API_KEY",
        "Content-Type": "application/json"
    };

    const response = await fetch(url, {
        method: 'POST',
        headers: headers,
        body: JSON.stringify({ input: articleText })
    });

    if (response.ok) {
        const data = await response.json();
        return data.topic;
    } else {
        throw new Error(`Request failed with status code ${response.status}`);
    }
}

// Example usage
const articleText = "This is a sample news article discussing the latest advances in artificial intelligence.";
classifyArticle(articleText).then(topic => {
    console.log(`The article is about: ${topic}`);
});

Frontend Example

Frontend Example

Next: How to improve more?

Challenges and Cautions

Accuracy and Bias: Current models may have biases based on their training data. It is essential to continually monitor the model outputs for any biased responses and update training datasets accordingly to minimize such occurrences.
Scalability Constraints: As the service grows, scalability might become a challenge. Consider implementing sharded, distributed processing of requests if demand increases significantly.
Data Privacy Concerns: Ensure that customer data handling practices are compliant with data protection regulations such as GDPR or CCPA. Regular audits and reviews can help maintain compliance.

Potential Improvements

Enhance Evaluation with LLM Judges: If runtime evaluations are not yet comprehensive, consider synthesizing hundreds of test cases to demonstrate readiness for production environments. Teammately Agents can assist in generating tailored LLM Judges to evaluate at scale.
Integration with Knowledge Bases: For improved user assistance capabilities, integrate the AI with external knowledge bases. For example, connect to widely-used systems like Wikipedia, specialized databases in specific domains (e.g., medical databases), or even proprietary knowledge stores. This could enhance the AI's ability to provide detailed, domain-specific information.
Optimize Using Smaller Models: To reduce operational costs and latency, experiment with smaller model architectures. Teammately Agents can be leveraged to iterate on model size while maintaining output quality, supported by continuous evaluations using LLM Judges.
Continuous Feedback Loop: Incorporate a feedback mechanism where real-time user interactions provide data for ongoing training and refinements. This ongoing cycle can create a more adaptive and robust system over time.
Expand Multi-Language Support: To reach a broader audience, enhance the model’s language capabilities to support more languages. Evaluate potential for additional language models or fine-tune existing ones to achieve this goal.
Adhering to these enhancements can significantly improve the overall functioning, scalability, and acceptance of the AI system. Regular evaluation and development will ensure that the AI remains competitive and effective in serving its intended purpose.
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