Is there a generalizable strategy for helping AI reason better? It seems that chain of reasoning and asking why questions help as well as critiquing through adversarial learning and inversion to flip the script. This would be useful for things like idioms and figures of speech that don’t make sense by themselves and so that would be a clue as to their double entender or irony > [!ai]+ AI > > You > ‘ve hit upon some excellent strategies for improving AI reasoning, particularly in the tricky > areas of idioms, figures of speech, and nuanced language. Let’s break > down a generalizable strategy, incorporating your insights and expanding upon them: > **I. Foundational Principles** > 1. **Context is King:** > AI needs to understand that meaning is heavily dependent on context. This includes: > - **Linguistic Context:** The surrounding words and sentences. > - **Situational Context:** The broader scenario or setting. > - **Cultural Context:** Shared knowledge, beliefs, and norms. > - **Speaker/Author Intent:** Understanding the goal or purpose behind the > communication. > 2. **Beyond Literal Interpretation:** AI must learn to move beyond surface-level, literal meanings to grasp figurative language, implied meanings, and subtext. > 3. **Knowledge Representation:** A robust and flexible > knowledge representation is crucial. This could involve: > - **Knowledge Graphs:** Connecting concepts, entities, and relationships. > - **Common Sense Databases:** Storing everyday knowledge about the world. > - **Embodied Knowledge:** (Potentially) Grounding language in simulated or real- > world experiences. > **II. Specific Techniques** > 1. **Chain-of-Thought (CoT) Prompting:** > - **Explicit Reasoning Steps:** Encourage the AI to break down its reasoning process into a series of logical steps. This makes its thought process transparent and easier to debug. > - **Example:** Instead of just asking “What does ‘kick the bucket’ mean?”, prompt with “Explain your reasoning step-by-step to determine the meaning of ‘kick the bucket’ in this sentence: ‘After a long and fulfilling life, the old man finally kicked the bucket.’” > **“Why” Questioning (Explanatory Reasoning):** > - **Probing for Justification:** After the AI provides an answer, ask follow-up “why” questions to force it to justify its reasoning. > - **Example:** “Why do you think ‘kick the bucket > ’ means ‘to die’ in this context? What clues in the sentence or your knowledge base support that interpretation?” > 3. **Adversarial Learning and Critique:** > - **Counter-Examples:** Present the AI with examples designed to challenge its current understanding. These could be cases where a literal > interpretation fails or where an idiom is used in an unusual way. > - **Self-Critique:** Train the AI to identify potential weaknesses or inconsistencies in its own reasoning. This could involve generating alternative interpretations and evaluating their plausibility. > - **Example:** “Here’s a sentence: ‘ > The news of his promotion really kicked the bucket.’ Does your interpretation of ‘kick the bucket’ still make sense here? Why or why not?” > 4. **Inversion/Flipping the Script:** > - **Perspective Taking:** Encourage the AI to consider the situation from different perspectives. > How would the meaning change if the speaker were different, or if the context were altered? > - **Negation:** Ask the AI to consider the opposite or negation of a statement to see if it can still reason correctly. > - **Example:** “If ‘kick the bucket’ means ‘ > to die,’ what would it mean if someone said ‘He didn’t kick the bucket’?” > 5. **Idiom and Figurative Language Specific Strategies:** > - **Dedicated Datasets:** Create or utilize datasets specifically focused on idioms, metaphors, similes, sarcasm, etc. > **Pattern Recognition:** Train the AI to recognize common patterns associated with figurative language (e.g., “as X as Y” for similes). > - **Metaphorical Reasoning:** Develop techniques to help the AI understand the underlying conceptual mappings in metaphors (e.g., “time is money”).