The Implications of Premise Ordering Effect on LLM Reasoning Performance
Premise ordering is a critical aspect of logical and mathematical reasoning tasks in AI, particularly with Large Language Models (LLMs). These models, such as GPT-4-turbo and PaLM 2-L, are found to have varying performance based on premise order. This ordering affects the models’ reasoning accuracy significantly, with a forward sequence yielding the best results. In addition, the inclusion of irrelevant context further impacts the models’ performance, suggesting distractibility. The R-GSM dataset, inclusive of 27K problems with different premise orders, demonstrates a decline in LLM accuracy, particularly in reordered problems. The findings indicate a need to refine AI’s reasoning capabilities to align more closely with human cognitive processes, ultimately leading to more versatile and reliable models capable of tackling real-world reasoning tasks. To learn more about the study, you can check out the paper on arXiv and stay updated with the researchers’ latest work via their Twitter and Google News accounts.