Causal reasoning is crucial for human intelligence, enhancing scientific reasoning and decision-making. Researchers have introduced CLADDER, a dataset to test formal causal reasoning in language models. This comprehensive dataset covers diverse causal queries, designed to evaluate and improve the causal reasoning capabilities of language models. The researchers also developed CausalCOT, a strategy to simplify causal reasoning problems and improve model performance. The study presents a challenging benchmark for assessing language models’ causal reasoning capabilities and addresses the limitations of previous works.
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Groundbreaking Approach to Causal Reasoning
Causal reasoning is a crucial aspect of human intelligence, leading to better scientific reasoning and rational decision-making. Researchers have introduced CLADDER, a dataset to test formal causal reasoning in language models (LLMs) through symbolic questions and ground truth answers.
CLADDER Dataset
CLADDER consists of over 10,000 causal questions covering diverse queries across the three rungs of the Ladder of Causation – associational, interventional, and counterfactual. The dataset also includes various causal graphs requiring different causal inference abilities. The researchers have provided ground-truth explanations with sequential reasoning and verbalized the questions and answers by turning them into stories. Additionally, step-by-step explanations have been generated to provide intermediate reasoning steps for better performance.
The dataset is balanced across graph structures, query types, stories, and ground-truth answers, with zero human annotation cost and minimal inferential costs for LLMs. The researchers have also designed CausalCOT, a chain-of-thought prompting strategy for simplifying causal reasoning problems by breaking them into simpler steps.
Evaluation and Results
Models like GPT, LLaMa, and Alpaca were evaluated on causal reasoning, with GPT-4 achieving an accuracy of 64.28% and CausalCOT outperforming with 66.64% accuracy. CausalCOT also improves reasoning abilities across all levels, with significant improvement on anti-commonsensical and nonsensical data, indicating its benefit for unseen data.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- This AI Paper Introduces a Groundbreaking Approach to Causal Reasoning: Assessing the Abilities of Language Models with CLadder and CausalCoT
- MarkTechPost
- Twitter – @itinaicom
