The ability of large language models (LLM) in logical reasoning has attracted much attention, and recent research has revealed a significant flaw: sensitivity to the order in which premise information is presented. Research shows that the order of premise information will significantly affect the inference accuracy of LLM, and disrupting the order may lead to a significant decrease in model performance. Researchers from Google DeepMind and Stanford have emphasized the importance of logical order and pointed out that this aspect is still an urgent challenge for LLM.
Recent research has found that large language models are affected by the order in which premise information is presented in logical reasoning tasks, and disorder may lead to performance degradation. Google DeepMind and Stanford researchers pointed out that the premise of logical and natural ordering can improve model performance. For models such as LLM, changing the order of premises will lead to performance degradation, which requires further research and solution. The order of premises has a significant impact on the inference performance of large language models and remains a challenge. Gemini, GPT-4, etc. have major flaws, and LLM performance has seriously declined.
All in all, LLM has obvious sequence dependencies in logical reasoning, which limits its application scope. Future research is needed to break through this bottleneck so that LLM can handle complex reasoning tasks more reliably. Improving LLM's ability to process prerequisite sequences is a key direction to improve its overall performance.