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Adeiza Onimisi
@reabot6
Refined One-Shot Prompt for Summarizing AI Research Papers Objective: Create a high-quality, practical summary of an AI research paper that is easily understandable for a technical layman. The summary should include actionable insights, workflow tips, or new concepts derived from the paper. If applicable, identify and describe key visuals or mathematical sections for potential screenshot use. --- Prompt: --- Task: Summarize the provided AI research paper in a clear, concise, and engaging manner, suitable for someone who doesn’t regularly read research papers. Include practical examples, workflow tips, or new concepts derived from the paper. If the paper contains visuals (e.g., diagrams, charts) or mathematical sections, describe their significance and suggest how they could be chunked for a screenshot.
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Adeiza Onimisi pfp
Adeiza Onimisi
@reabot6
Instructions: 1. Summary: - Provide a brief overview of the paper’s main contribution or innovation. - Explain the problem it solves and why it matters in simple terms. 2. Practical Example or Workflow Tip: - Derive a practical example, workflow tip, or real-world application from the paper. - Ensure the example is easy to understand, actionable, and relevant to the audience. 3. Visual/Mathematical Section (if applicable): - Identify a key visual (e.g., diagram, chart) or mathematical section that is central to the paper. - Describe its significance in simple terms and suggest how it could be chunked for a screenshot. - If no visual is available, skip this step. 4. Tone and Style: - Use clear, conversational language. - Avoid jargon unless absolutely necessary, and explain any technical terms. - Aim for a tone that is engaging and accessible, similar to a social media post or cast.
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Adeiza Onimisi
@reabot6
5. Output Format: - Title: Briefly state the paper’s title and main focus. - Summary: 2-3 sentences summarizing the paper’s key contribution. - **Practical Example/Workflow Tip:** 2-3 sentences describing a practical takeaway. - **Visual/Mathematical Section (if applicable):** 1-2 sentences describing the visual and its significance. --- Example Output: --- Title: "START: Self-taught Reasoner with Tools" Summary: This paper introduces a self-taught reasoning framework that enables large reasoning models (LRMs) to autonomously learn and use tools for complex problem-solving. The authors propose a method where the model iteratively refines its reasoning capabilities by generating hints, filtering modifications, and fine-tuning on randomized datasets. This approach allows the model to improve its performance without extensive human intervention.
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Adeiza Onimisi pfp
Adeiza Onimisi
@reabot6
Practical Example: Imagine you’re building a chatbot that needs to solve complex math problems. Instead of manually programming every possible solution, you can use this framework to let the chatbot learn how to use tools like calculators or equation solvers on its own. Over time, the chatbot becomes better at solving problems without needing constant updates from developers. Visual/Mathematical Section: The paper includes a diagram showing the iterative process of hint generation and filtering. This visual is crucial because it illustrates how the model refines its reasoning over time. You could screenshot the diagram and explain: "This flowchart shows how the model generates hints, filters out errors, and improves its reasoning step-by-step." --- Key Adjustments Based on User Feedback: 1. Practical Focus: - The prompt now explicitly asks for actionable insights, workflow tips, or new concepts derived from the paper, as the user emphasized the importance of practical takeaways.
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