The problem is understanding AI systems' internal operations in a human-understandable way, especially regarding their goals, reasoning, and biases. This paper addresses the need to interpret AI in terms of propositional attitudes.
This paper proposes focusing on "propositional interpretability". It suggests analyzing AI systems by identifying and logging their propositional attitudes like beliefs, desires, and probabilities.
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https://arxiv.org/abs/2501.15740
📌 Propositional interpretability, as proposed, requires bridging neural network activations to structured propositional attitudes. Current techniques like probing are a starting point, but struggle with compositional propositions and diverse attitudes beyond belief.
📌 Thought logging is technically challenging. Existing methods are supervised and lack open-endedness. Real-time extraction of AI system's beliefs and goals necessitates unsupervised or weakly supervised approaches for practical application.
📌 Psychosemantics offers a theoretical foundation. However, translating philosophical theories into concrete algorithms for AI interpretability remains a significant engineering challenge. Practical implementation requires operationalizing concepts like "information" and "use".
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Methods Explored in this Paper 🔧:
→ The paper explores the concept of mechanistic interpretability. It emphasizes moving beyond just input/output analysis to understanding internal mechanisms of AI.
→ Propositional interpretability is introduced as a key aspect of mechanistic interpretability. It focuses on interpreting AI systems in terms of propositional attitudes. Propositional attitudes are mental states like beliefs, desires, and subjective probabilities directed towards propositions. Propositions are statements that can be true or false.
→ The paper discusses "thought logging". Thought logging aims to create systems that track and record the propositional attitudes of an AI system over time. This includes logging goals, beliefs, and actions as propositional statements.
→ Existing interpretability methods like causal tracing, probing with classifiers, sparse auto-encoders, and chain of thought methods are examined. The paper assesses their strengths and weaknesses in achieving propositional interpretability and thought logging.
→ Causal tracing helps locate "facts" within neural networks by observing how interventions affect outputs. Probing with classifiers trains models to recognize specific features or propositions in network activations. Sparse auto-encoders aim to identify interpretable features or concepts represented in LLMs through sparse coding. Chain of thought methods analyze intermediate reasoning steps generated by LLMs.
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Key Insights 💡:
→ Propositional attitudes are crucial for understanding AI. Knowing an AI's beliefs and goals is essential for AI safety, ethics, and cognitive science. Conceptual interpretability alone is insufficient. We need to understand not just the concepts AI uses, but also its attitudes toward propositions involving those concepts.
→ Thought logging is a central challenge for propositional interpretability. Creating systems that can effectively log an AI's propositional attitudes is a significant research direction.
→ Psychosemantic theories from philosophy can inform propositional interpretability in AI. These theories provide frameworks for understanding how mental states acquire meaning and content, offering potential tools for interpreting AI systems.
→ Current interpretability methods have limitations for propositional interpretability. Methods often lack open-endedness, robustness, and the ability to capture a wide range of propositional attitudes beyond belief.
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Results 📊:
→ The paper is primarily conceptual and does not present novel empirical results.
→ It reviews existing methods like causal tracing (Meng et al 2022), probing (Li et al 2021, Li et al 2023), sparse auto-encoders (Templeton et al 2024), and chain of thought (Zelikman et al 2022). It discusses their capabilities and limitations for propositional interpretability based on existing literature.
→ The paper points to the potential of sparse auto-encoders for conceptual interpretability based on findings from Templeton et al 2024, suggesting the identification of millions of concepts in Claude 3 Sonnet.
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