NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·2h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP
TL;DR
Large language models (LLMs) can spread misinformation, impacting societal goals. This study evaluates three methods—SHAP, Rule Extraction, and RuleSHAP—to uncover belief-driven heuristics in LLMs.
✦ Why It Matters
Engineers can leverage these methods to enhance the interpretability of LLMs and mitigate misinformation risks.
Key Takeaways
- Utilize RuleSHAP for better rule extraction from LLMs.
- Understand belief-driven heuristics to combat misinformation.
- Test LLMs with injected behavioral triggers for insights.
- Leverage global SHAP scores to enhance model interpretability.
How It Works
RuleSHAP combines the strengths of global SHAP analysis, which quantifies feature importance, with rule induction techniques that generate explicit rules from these insights. By mapping LLM behaviors to numerical scores, RuleSHAP can effectively identify complex, non-univariate triggers that traditional methods often overlook.