Summary of Code Hallucination, by Mirza Masfiqur Rahman et al.
Code Hallucination
by Mirza Masfiqur Rahman, Ashish Kundu
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a technique called HallTrigger for generating arbitrary code hallucination using large language models (LLMs). The authors manually generated hallucinated code using LLMs, which often produces questionable correctness, authenticity, and reliability. This phenomenon is known as LLM hallucination, where the generated programs may not follow user requirements or contain errors. HallTrigger leverages three dynamic attributes of LLMs to craft prompts that can successfully trigger hallucinations without accessing model architecture or parameters. Results suggest that HallTrigger is effective in generating arbitrary code hallucination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are widely used for code generation, but the resulting programs often have serious issues with correctness and reliability. In this research, scientists created a way to generate fake code intentionally, using large language models. They made prompts that trigger the model to create incorrect or nonsensical code. This is important because it shows how widespread the problem of faulty code generated by AI is in software development. |
Keywords
» Artificial intelligence » Hallucination
