Why Fine-Tuning Encourages Hallucinations and How to Fix It

2026-04-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Large language models (LLMs) frequently hallucinate factually incorrect statements, with supervised fine-tuning (SFT) identified as a primary cause due to its impact on pre-existing knowledge. Researchers propose a self-distillation-based SFT method designed to reduce hallucinations by regularizing output-distribution drift, thereby facilitating new factual learning while preserving prior knowledge. Additionally, for scenarios where new knowledge acquisition is not required, freezing specific parameter groups can maintain task performance and decrease hallucinations by suppressing factual plasticity. The study investigates the underlying mechanisms of SFT-induced hallucinations, concluding that interference among overlapping semantic representations is a major driver, and the self-distillation approach effectively mitigates this interference.

Key takeaway

For AI Engineers and Research Scientists developing or fine-tuning LLMs, understanding that SFT can induce hallucinations by interfering with pre-trained knowledge is critical. You should consider implementing self-distillation techniques during SFT to preserve factual accuracy, or freeze model parameters when new factual acquisition is not a primary goal, to mitigate the risk of generating incorrect information.

Key insights

Supervised fine-tuning causes LLM hallucinations by interfering with pre-existing knowledge, which self-distillation can mitigate.

Principles

• SFT can degrade pre-training knowledge.
• Interference drives SFT-induced hallucinations.

Method

A self-distillation-based SFT method regularizes output-distribution drift to minimize hallucinations while learning new facts.

In practice

• Use self-distillation for SFT to reduce hallucinations.
• Freeze parameters if new factual knowledge is not needed.

Topics

• Large Language Models
• Supervised Fine-Tuning
• Model Hallucinations
• Continual Learning
• Self-Distillation

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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• Your LLM Has Hallucination Neurons. There Are Only a Handful of Them. A MUST READ — A recent finding identifies "H-Neurons," a minuscule subset comprising less than 0.1% of total neurons, as the causal drivers of hallucinations in large language models.
• Extrinsic Hallucinations in LLMs — LLM hallucinations arise from pre-training data issues and fine-tuning new knowledge, necessitating robust detection and mitigation strategies.
• Why Fine-Tuning Encourages Hallucinations and How to Fix It — SFT-induced hallucinations stem from factual forgetting due to semantic interference, mitigable by self-distillation or parameter freezing.
• Mechanisms of Prompt-Induced Hallucination in Vision-Language Models — VLMs hallucinate by prioritizing text over vision, a behavior traceable to specific, ablatable attention heads.
• Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models — SSCD mitigates VideoLLM hallucinations by using deliberately disrupted spatiotemporal and semantic negative features in contrastive decoding.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.