Hallucination Causes Lifecycle

Overview

A three-stage causal framework tracing hallucination origins through the LLM development pipeline: Data → Training → Inference. Each stage contributes distinct hallucination mechanisms, and understanding the stage helps target mitigation.

The Framework

Stage 1: Data-Level Causes

Problems with what the model learns from.

Misinformation & Bias

• Imitative falsehood: Model memorizes and reproduces false information from training data
• Societal biases: Gender, nationality, other biases create unfaithful outputs when triggered

Knowledge Boundaries

• Long-tail gaps: Rare information not well-represented in training
• Temporal limits: Knowledge cutoff creates fabrication risk for recent events
• Copyright restrictions: Legal constraints create systematic knowledge gaps

Alignment Data Quality

• New factual knowledge in SFT data exceeds model’s pre-training knowledge
• Complex/diverse instructions increase hallucination rates
• Task-specific formatting instructions may encourage hallucination

Stage 2: Training-Level Causes

Problems with how the model learns.

Pre-training Issues

• Unidirectional attention limits contextual dependency capture
• Soft attention dilution across long sequences
• Exposure bias: Training-inference gap causes error cascades

SFT Issues

• Capability-boundary mismatch: Model trained to answer beyond its knowledge
• No refusal training: Models don’t learn to say “I don’t know”
• Overfitting to formatting over factuality

RLHF Issues

• Sycophancy: Reward signal favors pleasing over truthful
• Both humans and reward models prefer confident, agreeable responses
• Internal belief can diverge from output behavior

Stage 3: Inference-Level Causes

Problems with how the model generates.

Decoding Strategy

• Randomness enables creativity but increases hallucination risk
• Higher temperature → more tail sampling → more hallucination
• Likelihood trap makes deterministic decoding produce low-quality text

Attention Drift

• Over-confidence in local context, under-attention to instructions
• Long outputs prone to “instruction forgetting”
• Context window position effects (lost in the middle)

Architectural Constraints

• Softmax bottleneck limits output distribution expressiveness
• Multi-modal distributions can’t be accurately represented

Reasoning Failures

• Reversal curse: “A is B” doesn’t guarantee “B is A”
• Multi-hop errors: Each reasoning step can compound
• Logical consistency breaks down over complex chains

How to Apply

Diagnosis: When a hallucination occurs, trace likely cause:

1. Is the false information prevalent online? → Data (imitative falsehood)
2. Is it a domain the model has gaps in? → Data (knowledge boundary)
3. Did the model ignore clear context? → Training (SFT) or Inference (attention)
4. Did it agree with a wrong premise? → Training (sycophancy)
5. Did reasoning break down mid-chain? → Inference (reasoning failure)

Mitigation Selection: Match intervention to stage:

• Data causes → Data filtering, RAG, model editing
• Training causes → Alignment improvements, refusal training, honesty objectives
• Inference causes → Decoding modifications, attention manipulation, verification loops

System Design: Different deployment contexts have different stage exposures:

• RAG systems: Data causes partially addressed, inference causes amplified
• Fine-tuned models: Training causes become dominant
• Prompt-engineered systems: Inference causes most relevant

Limitations

Causes often interact:

• Data gaps + SFT mismatch = confident fabrication
• Sycophancy + attention drift = agreeing with misread context
• Imitative falsehood + reasoning = elaborate wrong explanations

The lifecycle is a diagnostic aid, not a deterministic trace.

Related: 05-molecule—llm-hallucination-taxonomy, 05-molecule—capability-alignment-gap, 05-atom—rag-paradox-question