Hallucination in Large Language Models (LLMs) and Its Causes

Hallucination in Large Language Models (LLMs) and Its Causes

Practical Solutions for Hallucination in Large Language Models (LLMs)

Understanding Hallucinations

Large language models (LLMs) such as Llama, PaLM, and GPT-4 have revolutionized natural language processing but are prone to producing factually incorrect or inconsistent content. It’s crucial to understand the types and causes of hallucinations to mitigate their impact.

Types of Hallucinations

  • Factuality Hallucination: Involves discrepancies with real-world facts, including factual inconsistency and factual fabrication.
  • Faithfulness Hallucination: Refers to divergence from user instructions or provided context, including instruction inconsistency, context inconsistency, and logical inconsistency.

Causes of Hallucinations

  • Data-Related Causes: Stemming from flawed data sources, knowledge boundaries, and inferior data utilization.
  • Training-Related Causes: Including architecture flaws, exposure bias, and alignment issues.
  • Inference-Related Causes: Arising from decoding strategies and imperfect decoding representations.

Mitigation Strategies

Effective mitigation strategies include enhancing data quality, improving training processes, and employing advanced decoding techniques to reduce the occurrence of hallucinations.

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