Cost-effective hallucination detection through smart claim extraction beats expensive models
This paper analyzes different hallucination detection systems for LLMs, comparing their cost-effectiveness and diagnostic accuracy. It evaluates Pythia, LynxQA, and a baseline Grading strategy across summarization and question-answering tasks, revealing key trade-offs between performance and resource usage.
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https://arxiv.org/abs/2411.05270
🔍 Original Problem:
LLM hallucinations pose significant risks in real-world applications, leading to potential financial losses and legal liabilities. Current detection methods either rely on expensive human labeling or ineffective traditional metrics.
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🛠️ Solution in this Paper:
→ The paper introduces a comparative framework using diagnostic odds ratio (DOR) to evaluate hallucination detection systems.
→ Three strategies are analyzed: LynxQA using direct LLM evaluation, Pythia employing claim extraction and classification, and a baseline Grading approach.
→ Pythia breaks down text into claims and classifies them as entailment, contradiction, or neutral against reference material.
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💡 Key Insights:
→ Advanced models like GPT-4 show better performance but at 16.85x higher cost
→ Pythia maintains consistent performance across different model sizes
→ Strategy selection depends heavily on specific application needs and resource constraints
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📊 Results:
→ LynxQA with GPT-4 achieves 1.91x better DOR than baseline
→ Pythia achieves 1.28x better DOR than baseline at same cost
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