LLMs Do Not Think Step-by-step In Implicit Reasoning
Yijiong Yu
2024-11-26
Summary
This paper investigates how large language models (LLMs) perform reasoning tasks, specifically looking at whether they actually think step-by-step when using implicit reasoning methods.
What's the problem?
While using a method called Chain-of-Thought (CoT) has been shown to improve LLM performance on complex tasks, it can slow down the model and increase computing costs. As a result, researchers have explored implicit reasoning methods that don't require the model to explicitly show each step of its thinking. However, there is uncertainty about whether these implicit methods are as effective as explicit CoT methods.
What's the solution?
The authors conducted experiments to examine how LLMs handle implicit reasoning by analyzing the hidden states of the model during the process. They found that LLMs do not genuinely engage in step-by-step reasoning when using implicit methods; instead, they seem to rely on previous experiences rather than following a structured thought process. This suggests that while implicit reasoning might be faster, it is less reliable than explicit reasoning methods.
Why it matters?
This research is important because it highlights the limitations of implicit reasoning in LLMs and reinforces the value of explicit reasoning techniques like CoT for complex tasks. Understanding how these models think can lead to better designs and improvements in AI systems, ensuring they perform more effectively in real-world applications.
Abstract
It has been well-known that Chain-of-Thought can remarkably enhance LLMs' performance on complex tasks. However, because it also introduces slower inference speeds and higher computational costs, many researches have attempted to use implicit CoT, which does not need LLMs to explicitly generate the intermediate steps. But there is still gap between their efficacy and typical explicit CoT methods. This leaves us a doubt that, does implicit CoT really equal to explicit CoT? Therefore, in this study, we address this question through experiments. We probe the information of intermediate steps from the model's hidden states when it is performing implicit CoT. The results surprisingly indicate that LLMs hardly think about intermediate steps, suggesting they may just rely on experience rather than strict step-by-step reasoning. Moreover, we find LLMs' implicit reasoning capabilities are susceptible and unstable, reaffirming the necessity of explicit CoT to effectively support complex tasks.