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Code Debugging with LLMs
A Large Language Model that has been trained on extensive datasets of both source code and natural language can be effectively utilized for the task of code debugging.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Code Debugging with LLMs
Analyzing Types of Textual Correction
A language model is tasked with correcting the following sentence: "To improve the car's aerodynamics, the engineers decided to increase its weight and add a large, flat spoiler." Which of the following options best analyzes the error and proposes a logical correction?
Evaluating an LLM's Text Correction
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A developer has written the Python function below to find the smallest number in a list of positive integers. When they test it with the list
[10, 3, 8, 5], the function incorrectly returns0instead of the expected3.def find_smallest_number(numbers): smallest_so_far = 0 for num in numbers: if num < smallest_so_far: smallest_so_far = num return smallest_so_farThey ask a large language model for help debugging it. Analyze the four potential responses from the model. Which response provides the most accurate and helpful explanation of the logical error?
Crafting an Effective Debugging Prompt
A developer is working on four different code snippets, each containing a bug. In which of the following scenarios would a large language model, trained on a vast corpus of code, be most effective at identifying and suggesting a fix for the error?