In our experiments, we leveraged the Natural Program-based approach to engage language models in deductive reasoning tasks, allowing for detailed verification of logical steps in reasoning.
The performance of ChatGPT in identifying ungrounded information was promising, showcasing its ability to catch logical errors, thus proving useful in maintaining reasoning integrity.
Despite some successes, we found limitations in the model’s capability, particularly in scenarios involving ungrounded premise numbers, where it mistakenly derives conclusions without proper grounding.
Our findings underscore the necessity for continuous improvement in deductive verification accuracy, as models can still struggle with recognizing grounded versus ungrounded number assumptions.
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