Research on Reasoning Capabilities of Large Language Models: Comparative Analysis of Human Thinking and Logical Reasoning

This article focuses on the reasoning capabilities of large language models, comparing the differences between human intuitive reasoning and logical reasoning. It analyzes the performance of models represented by T5 in reasoning tasks, existing cognitive biases, challenges and innovations in evaluation methods, as well as technical paths to improve reasoning capabilities and ethical considerations for applications, and discusses the development direction of model reasoning capabilities.

Human cognitive science divides reasoning into intuitive reasoning (fast, automatic, relying on experiential heuristics) and logical reasoning (slow, conscious, following deductive rules). Large language models exhibit unique hybrid characteristics: on one hand, they can quickly generate reasonable answers similar to human intuition; on the other hand, they have systematic flaws when facing multi-step complex problems.

Google's T5 series models provide an experimental platform for reasoning research. Performance differences among models of different scales (from T5-base to T5-11B) in reasoning benchmark tests show: scale growth leads to improved reasoning capabilities, but non-linearly; the gap between small and large models is not obvious in common sense reasoning tasks, while the scale effect is more significant in mathematical/symbolic reasoning tasks.

Human reasoning has cognitive biases such as confirmation bias and anchoring effect. Experiments show that language models also exhibit similar biases: such as the framing effect (when the problem statement changes, the logic is the same but the answer changes); they are highly sensitive to the statistical patterns of training data, leading to poor performance on rare but logically correct reasoning paths.

Traditional accuracy metrics are difficult to distinguish between true reasoning and pattern matching. Researchers have developed multi-dimensional evaluation strategies: adversarial testing to check robustness; compositional generalization testing to evaluate adaptability to new combinations; causal reasoning testing to focus on the ability to understand causal relationships between variables—all together painting a portrait of model reasoning capabilities.

To address the limitations of model reasoning, improvement directions include: chain-of-thought prompting (guiding the display of intermediate steps to enhance complex task-solving capabilities); retrieval-augmented generation (combining external knowledge bases to obtain accurate reasoning premises); exploration of specialized reasoning training data, multi-task learning, neural-symbolic fusion, etc.

The model reasoning mechanism has guiding significance for applications in fields such as law, medicine, and finance, and its capability boundaries need to be clearly defined. Be alert to the risk of over-reliance: when model reasoning is opaque or has systematic biases, using it for key decisions may have serious consequences. It is recommended to establish a human-machine collaborative hybrid decision-making mechanism to complement the model's computational advantages with human judgment.

Research on large language model reasoning is developing rapidly, and models are gradually narrowing the gap with human reasoning, but general artificial intelligence requires a deep understanding of the essence of reasoning. Future research needs to improve capabilities while paying more attention to interpretability, controllability, and fairness to ensure it serves human well-being.