Hallucinations: Why Your LLM Makes Things Up and How to Stop It

Large Language Model (LLM) hallucinations, where models generate factually incorrect or misleading information, pose significant risks across various critical domains. These inaccuracies stem from issues throughout the LLM lifecycle, including noisy training data, imperfect fine-tuning, and inference-time limitations. Addressing this challenge is crucial for ensuring reliable LLM deployment and mitigating potential harm to users and businesses.

Understanding LLM Hallucinations

• Hallucination refers to LLMs producing factually incorrect, fabricated, or misleading content.
• Typical examples include providing wrong geographical facts or fabricating non-existent research papers.
• Hallucinations are broadly classified into Factual Conflict, Fabrication (making things up), Instruction Misinterpretation, and Logical Errors.

Causes of Hallucinations Across the LLM Lifecycle

• Pre-training: Issues include noisy, biased, or outdated training data, lack of specific domain knowledge, and optimization for linguistic fluency over factual accuracy.
• Supervised Fine-tuning (SFT) & Reinforcement Learning with Human Feedback (RLHF): Annotation errors, inconsistencies, overfitting, and imperfect reward designs can lead models to confidently generate incorrect information.
• Inference: The token-by-token generation process prevents early error correction, leading to a "snowball effect," and random sampling strategies can increase the risk of generating inaccurate content.

Strategies for Hallucination Mitigation

• Retrieval-Augmented Generation (RAG): This approach enhances accuracy by integrating external, up-to-date knowledge sources, shifting the LLM's role from a knowledge source to an analyzer of retrieved information.
• Post-hoc Hallucination Detection: Involves both "white-box" methods (analyzing internal model states like uncertainty or hidden states) and "black-box" methods (external checks such as rule-based validation, external tool augmentation, or using specialized detection models).
• Comprehensive Lifecycle Management: Solutions can be applied across the LLM lifecycle, from data cleaning in pre-training to "honesty-oriented" samples in fine-tuning, though most current efforts focus on the inference stage due to cost.

Real-World Implications and Solutions

• Hallucinations pose significant real-world risks, potentially misleading users in critical sectors (e.g., legal, medical, finance) and exposing businesses to legal disputes, reputational damage, and compliance issues.
• Industry initiatives, such as China's "Qinglang rectification action," emphasize the strict control of AI hallucination.
• Volcengine's cloud security team has implemented a specific hallucination risk detection solution for RAG scenarios, which analyzes and compares model responses against source knowledge to identify factual conflicts.