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No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes
arXiv – CS AI|Iv\'an Vicente Moreno Cencerrado, Arnau Padr\'es Masdemont, Anton Gonzalvez Hawthorne, David Demitri Africa, Lorenzo Pacchiardi||1 views
🤖AI Summary
Researchers developed linear probes that can predict whether large language models will answer questions correctly by analyzing neural activations before any answer is generated. The method works across different model sizes and generalizes to out-of-distribution datasets, though it struggles with mathematical reasoning tasks.
Key Takeaways
- →Linear probes can predict LLM answer accuracy from question-only activations before token generation begins.
- →The predictive method generalizes across model families from 7B to 70B parameters and works on diverse knowledge datasets.
- →Predictive power peaks in intermediate neural network layers rather than final layers.
- →The approach fails to generalize effectively on mathematical reasoning questions.
- →Models saying 'I don't know' show strong correlation with probe confidence scores, indicating the same mechanism captures both correctness and uncertainty.
#llm#machine-learning#ai-research#neural-networks#model-interpretability#predictive-accuracy#arxiv#linear-probes
Read Original →via arXiv – CS AI
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