The Impact of Premise Order on Large Language Model Reasoning Performance

Core Concepts

Large language models exhibit significant performance variations based on the order of premises, impacting reasoning accuracy.

Abstract

The content explores how the ordering of premises affects the reasoning performance of large language models (LLMs). It highlights that LLMs perform best when the premise order aligns with the ground truth proof, showcasing a significant drop in accuracy when the order is altered. The study extends to mathematical reasoning, revealing similar impacts on model performance.

Stats

Permuting premise order can cause a performance drop of over 30%.
All LLMs fail to generate proof after changing the order of relevant rules.
GPT-4-turbo and PaLM 2-L achieve decent performance without distracting rules but further decrease in accuracy with added distractions.
Error analysis shows fact hallucination as a common error pattern across all LLMs.
R-GSM dataset contains 220 pairs of problems with reordered problem descriptions.

Quotes

"We show that LLM tendencies resemble human preference w.r.t. premise order."
"LLMs are much more susceptible to such ordering effects."
"The premise ordering effect indicates that LLMs are more comfortable reasoning via reading left-to-right instead of back-and-forth."

Key Insights Distilled From

Premise Order Matters in Reasoning with Large Language Models

by Xinyun Chen,... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2402.08939.pdf

Deeper Inquiries

How can training techniques be adapted to mitigate the premise order effect in large language models?

To address the premise order effect in large language models (LLMs), several training techniques can be adapted:

Data Augmentation: Introducing variations in the ordering of premises during training can help LLMs learn to generalize better across different permutations. By exposing the model to diverse input sequences, it becomes more robust to changes in premise order during inference.

Curriculum Learning: Gradually increasing the complexity of problems by introducing shuffled or reversed orders of premises can help LLMs adapt to different arrangements over time. This gradual exposure allows the model to learn how to reason effectively regardless of the initial sequence.

Prompt Engineering: Crafting prompts that explicitly guide the model on how to process information irrespective of its order can aid in mitigating this effect. Providing cues or instructions within prompts that emphasize focusing on relevant information rather than sequential presentation can improve performance.

Fine-Tuning Strategies: Fine-tuning LLMs on tasks specifically designed to challenge their ability with varied premise orders can enhance their capability for reasoning under different conditions. Task-specific fine-tuning focused on handling permutation-invariant reasoning tasks could be beneficial.

Architectural Modifications: Modifying LLM architectures by incorporating mechanisms like attention modules that are less sensitive to positional information may reduce reliance on specific word orders and improve performance across various permutations.

By implementing these adaptations during training, LLMs can become more adept at reasoning with varying premise orders, ultimately enhancing their overall performance and generalization capabilities.

What implications does this study have for real-world applications relying on large language models?

The findings from this study have significant implications for real-world applications leveraging large language models (LLMs):

Enhanced Model Robustness: Understanding and addressing the premise order effect will lead to more robust and reliable LLMs when deployed in practical scenarios where data inputs may not always follow a consistent structure or sequence.

Improved Accuracy and Consistency: By mitigating sensitivity towards premise ordering, LLMs are likely to deliver more accurate and consistent results across a wide range of tasks, ensuring higher quality outputs for users relying on these models.

Increased Trustworthiness: Minimizing errors caused by incorrect handling of varying premise orders enhances user trust in LLM-generated outputs, especially in critical domains such as healthcare diagnostics, legal document analysis, or financial forecasting where accuracy is paramount.

Broader Applicability : The ability of LLMs trained with consideration for differing premised orders extends their applicability across diverse industries and use cases without compromising performance consistency.

How might understanding premise order preferences in Large Language Models contribute advancements in cognitive science?

Understanding how Large Language Models (LLMs) process information based on varying premises' sequencing offers insights into cognitive processes:
1 .Human Reasoning Comparison: Studying how humans prefer certain sequences aids researchers' comprehension about human cognition patterns while solving logical problems involving multiple premises.
2 .Model Interpretation: Analyzing why certain ordering preferences exist within an AI system sheds light on internal decision-making processes within neural networks.
3 .Cognitive Bias Exploration: Identifying biases exhibited by AI systems due to preference towards specific sequences provides parallels between machine learning behavior and human cognitive biases observed through psychological studies.
4 .Algorithmic Development: Leveraging knowledge about preferred premised-order effects enables researchers developing new algorithms inspired by both machine learning principles and human cognition theories.
5 .Educational Insights: Applying findings from studying premised-order effects helps design educational tools that align with natural cognitive inclinations when presenting complex concepts sequentially.