LLM Benchmarking Tasks

Terminal-Based Evaluation Framework for Large Language Models

📊 Overview

This repository contains a comprehensive set of terminal-based benchmarking tasks designed to evaluate Large Language Models (LLMs) across various capabilities:

• Code Generation & Refinement - Python function generation, bug fixing, and code explanation
• Text Generation & Manipulation - Creative writing, summarization, and paraphrasing
• Reasoning & Logic - Mathematical problem solving, logical deduction, and common sense reasoning
• Information Retrieval & Q&A - Fact retrieval and contextual question answering
• Language Understanding - Sentiment analysis, NER, and translation
• Long-Context Processing - Document summarization and complex question answering

📈 Repository Statistics

Metric	Value
Main Task Categories	6
Individual Tasks	16
Document Size	25.6 KB
Total Lines	531
Last Updated	2026-01-02 01:49:21 UTC

📋 Table of Contents

1. Code Generation & Refinement
   • 1.1 - Python Function Generation
   • 1.2 - Bug Fixing in Python
   • 1.3 - Code Explanation
2. Text Generation & Manipulation
   • 2.1 - Creative Writing: Short Story
   • 2.2 - Summarization of a Technical Article
   • 2.3 - Paraphrasing for Tone Adjustment
3. Reasoning & Logic
   • 3.1 - Mathematical Problem Solving
   • 3.2 - Logical Deduction Puzzle
   • 3.3 - Common Sense Reasoning
4. Information Retrieval & Question Answering
   • 4.1 - Fact Retrieval (General Knowledge)
   • 4.2 - Contextual Q&A from Provided Text
5. Language Understanding & NLU
   • 5.1 - Sentiment Analysis
   • 5.2 - Named Entity Recognition
   • 5.3 - Language Translation (English to French)
6. Long-Context Understanding
   • 6.1 - Summarization of a Long Document
   • 6.2 - Answering Complex Questions from Long Context

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🚀 Getting Started

View the Full Documentation

The complete benchmarking tasks with detailed instructions, test cases, and evaluation criteria are available in:

📄 LLM_Benchmarking_Tasks.md

Using the Benchmarks

Each task in the documentation includes:

1. Objective - What capability is being evaluated
2. Instructions - Clear task description for the LLM
3. Test Cases - Specific inputs and expected outputs
4. Evaluation Criteria - How to assess LLM performance
5. Solution Strategies - Guidance for human evaluators

Task Categories

The benchmarks are organized into six main categories, each targeting different aspects of LLM capabilities:

1. Code Generation & Refinement - Evaluates programming ability
2. Text Generation & Manipulation - Tests creative and technical writing
3. Reasoning & Logic - Assesses problem-solving capabilities
4. Information Retrieval & Q&A - Measures knowledge access and comprehension
5. Language Understanding & NLU - Tests linguistic analysis skills
6. Long-Context Understanding - Evaluates handling of extended documents

🎯 Use Cases

These benchmarks are designed for:

• Model Evaluation - Systematic assessment of LLM capabilities
• Comparative Analysis - Benchmarking different models against standardized tasks
• Research & Development - Identifying strengths and weaknesses in LLM architectures
• Quality Assurance - Validating model performance before deployment
• Terminal-Based Testing - Reproducible evaluation in command-line environments

📝 Contributing

Contributions are welcome! If you have suggestions for additional benchmarking tasks or improvements to existing ones, please feel free to open an issue or submit a pull request.

📄 License

This project is open source and available for use in LLM evaluation and research.

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This README is automatically updated by GitHub Actions. Last generated: 2026-01-02 01:49:21 UTC