Systematic Comparison of Logical Reasoning Abilities in Humans and Large Language Models

Keskeiset käsitteet

Larger language models display more accurate logical reasoning than humans, but still exhibit systematic biases mirroring human reasoning errors.

Tiivistelmä

The authors conducted a detailed study comparing the syllogistic reasoning abilities of humans and large language models (LMs) from the PaLM 2 and Llama 2 families. Key findings:

Larger LMs are more accurate in logical reasoning than humans on average, but their accuracy is still far from perfect, with systematic errors on certain syllogism types.

LMs exhibit several biases that mirror human reasoning, such as sensitivity to the ordering of variables in the premises (the "figural effect") and making confident but incorrect inferences (syllogistic fallacies).

Using the Mental Models theory of human reasoning, the authors find that larger LMs display more deliberative reasoning strategies compared to smaller models, which is partly dissociable from their higher accuracy.

While LMs learn from human-generated text and thus may be expected to replicate human biases, the authors find that LMs can sometimes overcome certain human reasoning errors, suggesting that the training data and objectives may provide sufficient signal to improve logical reasoning beyond the human level.

The results highlight the tension between developing AI systems that reason accurately versus those that reason similarly to humans, and suggest that cognitive science frameworks can provide valuable insights into understanding and interpreting the reasoning capabilities of large language models.

Tilastot

Some key statistics from the paper:
"LMs draw correct inferences more often than humans, and larger LMs tend to be more accurate than smaller ones, but the accuracy of even the best performing LM is only about 75%, and scale does not consistently lead to accuracy gains."
"LM errors are systematic, with very low accuracy on particular syllogism types."
"Like humans, LMs are sensitive to the ordering of terms in the premises of a syllogism even when it is logically irrelevant."
"LMs show many of the same syllogistic fallacies, characterized by high confidence and low accuracy, as humans."

Lainaukset

"Larger LMs are more logical than smaller ones, and also more logical than humans."
"LMs often mimic the human biases included in their training data, but are able to overcome them in some cases."
"Larger LMs behave more like mReasoner instantiations with high SYSTM2 and WEAKEN values, as indicated by the fact that their first principal component is higher; in the terminology of Khemlani and Johnson-Laird (2016), they show a stronger behavioral signature of deliberative reasoning."

Tärkeimmät oivallukset

A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models

by Tiwalayo Eis... klo arxiv.org 04-12-2024

https://arxiv.org/pdf/2311.00445.pdf

Syvällisempiä Kysymyksiä

How do the reasoning biases exhibited by language models compare to those of other AI systems, such as reinforcement learning agents or symbolic reasoning systems?

In comparison to other AI systems like reinforcement learning agents or symbolic reasoning systems, language models exhibit reasoning biases that are more closely aligned with human biases. Language models, trained on large text corpora, tend to replicate the biases present in the training data, which often reflect human beliefs and inferences. These biases can manifest in various ways, such as sensitivity to the ordering of terms in a syllogism, drawing confident but incorrect inferences, and displaying systematic errors similar to those observed in human reasoning.
Reinforcement learning agents, on the other hand, may exhibit biases related to reward structures or exploration-exploitation trade-offs. These biases are more task-specific and may not directly mirror human reasoning biases. Symbolic reasoning systems, which operate based on predefined rules and logic, are designed to follow normative logical reasoning principles and are less likely to exhibit human-like biases. However, they may still face challenges in handling uncertainty or complex real-world data.
Overall, language models show biases that are more reflective of human reasoning tendencies, while other AI systems may exhibit biases that are more task-specific or inherent to their design principles.

How do the reasoning biases exhibited by language models compare to those of other AI systems, such as reinforcement learning agents or symbolic reasoning systems?

The reasoning biases exhibited by language models can be attributed to various aspects of the training process and model architecture. One key factor is the training data itself, which is primarily generated by humans and may contain inherent biases, errors, or inconsistencies. Language models learn from this data and may inadvertently replicate the biases present in the training corpus.
Additionally, the architecture of language models, such as transformer models, can also contribute to deviations from normative logical reasoning. The self-supervised learning objectives used during training may prioritize predictive performance over logical consistency, leading the models to prioritize patterns in the data that optimize performance metrics rather than adhere strictly to logical rules.
To address these issues and improve the logical competence of language models, several approaches can be considered. One approach is to incorporate explicit constraints or objectives during training that encourage logical reasoning and penalize illogical inferences. This can help guide the model towards more accurate and consistent reasoning patterns. Additionally, diversifying the training data to include a wider range of perspectives and sources can help mitigate biases present in the data and promote more robust reasoning capabilities.

How might AI systems be designed to strike an optimal balance between human-likeness and accuracy in reasoning, depending on the specific application domain?

Designing AI systems to strike an optimal balance between human-likeness and accuracy in reasoning requires a nuanced approach that considers the specific requirements and constraints of the application domain. In domains where human intuition and interpretability are crucial, such as healthcare or legal decision-making, prioritizing human-likeness in reasoning may be more important. This can involve incorporating cognitive models of human reasoning, such as Mental Models Theory, to guide the AI system's decision-making process and ensure alignment with human expectations.
On the other hand, in domains where precision and accuracy are paramount, such as autonomous driving or financial forecasting, prioritizing logical accuracy in reasoning may take precedence. AI systems in these domains can be designed to follow strict logical rules and principles, minimizing human biases and errors in decision-making processes.
To achieve an optimal balance between human-likeness and accuracy, AI systems can be tailored to the specific needs of the application domain through a combination of data curation, model architecture design, and evaluation metrics. By carefully considering the trade-offs between human-like reasoning and logical accuracy, AI systems can be optimized to perform effectively and ethically in diverse real-world scenarios.