The study examines how the order of premises impacts reasoning in large language models (LLMs) present in AI. It finds that LLM performance is significantly affected by premise order, with deviation leading to a performance drop of over 30%. The research aims to refine AI’s reasoning capabilities to align better with human cognition.
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Decoding AI Reasoning: The Impact of Premise Ordering on Large Language Models
Understanding the Challenge
Human cognition involves logical deduction, where conclusions are drawn from a set of facts. In AI, Large Language Models (LLMs) face challenges in reasoning accurately when the sequence of presented premises is altered. This impacts their performance significantly.
Research Insights
Studies by Google Deepmind and Stanford University reveal that LLM reasoning performance is sensitive to the ordering of premises. Altering the sequence can lead to a performance drop of over 30%, highlighting an underexplored aspect of model sensitivity.
Measuring the Effect
The impact of premise ordering on LLM reasoning performance is measured by varying the number of rules required in the proof and the number of distracting rules. The R-GSM benchmark demonstrates a decline in LLM accuracy, particularly with reordered problems, highlighting issues such as fact hallucination and errors arising from sequential processing and overlooked temporal order.
Path Forward
Addressing the limitation of premise ordering is crucial for refining AI’s reasoning capabilities to align with human thought processes, ultimately leading to more versatile and reliable models capable of navigating real-world reasoning tasks.
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