Evaluating Analogical Reasoning in LLMs

A study conducted by Martha Lewis and Melanie Mitchell examines if LLMs’ analogical reasoning is comparable to human-like reasoning. By testing ‘counterfactual’ scenarios - versions of problems akin to what humans solve but dissimilar from expected training data - they found a stark contrast between human consistency and LLMs’ performance dips.

• Comparison: Humans vs. GPT models on original and counterfactual analogy problems.
• Performance Discrepancy: Notable decline in LLMs’ abilities to tackle counterfactual problems.
• Insight: Suggests LLMs may not possess the humanlike abstract analogical reasoning capabilities.
• Conclusions: Despite LLMs’ success on some fronts, they might rely on processes that match seen data rather than true abstract reasoning skills.
• Abstract Reasoning: Uncovers the gap between human and LLM analogical reasoning, encouraging efforts to instill more genuine abstraction in AIs.

The findings suggest that while LLMs can imitate certain types of human reasoning, they often fail to generalize, pointing towards the necessity for models that can truly understand and process analogies as humans do.