New study questions LLM causal reasoning benchmarks

A new research paper published on arXiv questions the validity of the CLadder benchmark for evaluating causal reasoning in large language models (LLMs). The study, conducted by Cladder & Jansen, found that superficial patterns within the procedurally generated data sets, rather than genuine causal reasoning, likely contribute to the high performance observed in LLMs. Even simpler models like random forests achieved strong results on CLadder-generated data, and LLM performance remained brittle when critical reasoning elements were removed, suggesting LLMs are exploiting superficial patterns rather than demonstrating complex causal understanding. AI

IMPACT Raises concerns about the reliability of current benchmarks for assessing LLM causal reasoning, potentially impacting future model development and evaluation.

RANK_REASON Research paper published on arXiv evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• large language models (LLMs)
• random forests

• paper
• safety

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