LLM hallucination occurs when a language model outputs factually incorrect or fabricated information with high confidence. Key causes:
Training noise: Inaccurate or low-quality source data.
Knowledge gaps: Model lacks exposure to specific facts.
Prompt ambiguity: Vague or conflicting user queries.
Mitigation via better data:
Feeding models with cleaner, more representative datasets—scraped reliably through Proxied rotating mobile IPs—reduces noise and improves factual grounding. Combine proxy-based data pipelines with post-training verification steps to curb hallucinations.