When AI Codes Like a Parrot: LLMs Mimic Buggy Code with Impressive Consistency
LLMs are bug replicators, not fixers. According to recent research, these AI models often regurgitate buggy code rather than correct it, echoing errors like a karaoke machine stuck on a bad tune. When faced with flawed code, LLMs might just as well be singing, “Oops, I did it again!”
Hot Take:
Looks like our AI overlords aren’t quite ready to take over just yet. Instead of fixing our coding mishaps, they’re content to just copy our homework—errors and all! Maybe we should just stick to asking them to write our grocery lists instead of our code.
Key Points:
- Large Language Models (LLMs) often regurgitate buggy code rather than fix it.
- The study involved seven different LLMs, including OpenAI’s GPT models and Google’s Gemma-7B.
- LLMs have nearly equal chances of producing correct or buggy code in error-prone contexts.
- 44% of the bugs made by LLMs are identical to historical bugs.
- Researchers suggest better integration with development tools and improved error detection is needed.
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