Verified65% confidenceFactExact time
Hallucination problems in LLM code generation manifest as fabricating non-existent APIs, incorrectly combining parameter orders of real APIs, or generating deprecated usage based on outdated documentation.
3
Sources
65%
Confidence
Long-term
Relevance
7/28/2025
First Seen
Sources
ABCoder in Practice: A Demonstration of Solving AI Code Hallucination
bilibili字节跳动开源7/28/2025
Related Entities
Related Claims
UnverifiedModels may "hallucinate" non-existent APIs or configuration options, causing runtime errors.78% similarUnverifiedIn data pipeline debugging scenarios, LLM hallucinations directly manifest as incorrect root cause identification, potentially misleading engineers in their troubleshooting.74% similarUnverifiedMost of what goes wrong in LLM features is not in the model itself but in the surrounding code, such as building the messages list, selecting the model ID, parsing JSON, handling an empty choices list, retrying on 429s, and counting tokens.71% similarUnverifiedLLM-based code auditing is limited by the context window, may produce hallucinated false positives, and struggles to guarantee reproducible results.71% similarUnverifiedLLMs do not maintain a factual database and lack the metacognitive ability to know 'whether an operation was actually performed'; when the context strongly implies a certain output pattern, they generate text conforming to that pattern with high confidence, even if the content is entirely fabricated.71% similar
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