Probing Language Models on Their Knowledge Source
Abstract:
This paper investigates how language models handle different sources of knowledge, particularly focusing on the interplay between parametric and contextual knowledge. We develop probing techniques to understand when models rely on their training data versus contextual information, with implications for mitigating hallucinations in language models. Our approach combines mechanistic interpretability with controlled experiments to analyze knowledge source attribution in transformer architectures.
Key Contributions
- Novel probing methodology for knowledge source attribution in language models
- Analysis of parametric vs contextual knowledge conflicts and their resolution
- Framework for understanding model knowledge behavior under different conditions
- Insights for hallucination mitigation strategies through knowledge source control
- Empirical evaluation on multiple model architectures and knowledge domains
Methodology
We employ mechanistic interpretability techniques combined with controlled probing experiments to analyze how language models process and integrate different knowledge sources. Our approach includes systematic evaluation of model responses under various knowledge conflict scenarios, where parametric knowledge (from training) conflicts with contextual information.
The methodology involves designing specific probes that can distinguish between different knowledge sources, using attention analysis and activation patterns to understand the internal mechanisms of knowledge processing. We evaluate our approach on multiple transformer architectures and knowledge domains to ensure generalizability.
Key Findings
- Language models show distinct patterns when processing parametric vs contextual knowledge
- Attention mechanisms play a crucial role in knowledge source attribution
- Models exhibit different behaviors when faced with conflicting knowledge sources
- Probing techniques can effectively identify knowledge source preferences
Impact
This work contributes to mechanistic interpretability research by providing insights into how language models handle knowledge conflicts. The findings have direct applications to reducing hallucinations and improving model reliability, particularly in scenarios where models must balance different sources of information.
The probing techniques developed in this work can be used by researchers and practitioners to better understand model behavior and develop more robust language models that can handle conflicting information more effectively.