Do LLMs Really Think Step-by-step In Implicit Reasoning? (2411.15862v4)

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. However, the invisible reasoning process leaves us a doubt that, can implicit CoT really be 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 either trained or prompted to perform implicit CoT. The results surprisingly indicate that when prompted, LLMs hardly think about intermediate steps, suggesting they may just rely on experience rather than strict step-by-step reasoning. But when trained, they indeed calculate intermediate steps. Moreover, in both situations, we find the effect of using implicit CoT is susceptible to the format of the problem, reaffirming the current deficiency of implicit CoT.

• The paper demonstrates that LLMs do not perform true step-by-step reasoning when using implicit methods.
• Using multi-step arithmetic tasks and linear probing, the study shows that implicit reasoning struggles with reliability compared to explicit Chain-of-Thought approaches.
• Experimental tweaks like reversing input order reveal that LLMs' implicit reasoning degrades under subtle changes, highlighting the benefits of explicit reasoning methodologies.

Analysis of Implicit and Explicit Reasoning in LLMs

The paper "LLMs Do Not Think Step-by-step In Implicit Reasoning" by Yijiong Yu explores the distinctions and efficiencies of implicit versus explicit reasoning within LLMs. This research investigates the efficacy of Chain-of-Thought (CoT) prompting and reveals significant insights regarding implicit reasoning methods.

Chain-of-Thought has shown notable success in enhancing the reasoning capabilities of LLMs, facilitating the demand for explicit intermediate reasoning steps in solving complex tasks. However, the computational efficiency drawbacks of explicit CoT have prompted researchers to explore implicit CoT strategies that bypass outputting these intermediate steps. This paper addresses whether implicit CoT can be considered equivalent to explicit CoT, specifically highlighting gaps in performance and understanding.

The experiments leverage a multi-step arithmetic problem-solving scenario using the Qwen2.5-72B-Instruct model. This setup examines whether hidden states in LLMs reflect intermediate reasoning steps without explicitly outputting them. The paper's findings indicate that LLMs generally do not engage in step-by-step reasoning in the implicit mode. Instead, they seem to intuitively arrive at answers, bypassing intermediate calculations. This suggests that implicit reasoning may not be a true substitute for explicit reasoning due to its susceptibility and instability.

Key Findings and Methodology

The investigation uses a large model (72B parameters) to probe simple arithmetic problems, identifying hidden states' ability to predict intermediate results through linear probing methods. The significant results indicate:

1. Probing Intermediate Steps: It was observed that while the model memorizes starting values and can produce a final answer, it does not consistently compute intermediate steps, leaving a gap in reliable multi-step reasoning.
2. Sensitivities in Implicit Reasoning: Further tests showed drastic performance degradation when minor modifications, such as reversing task order or scaling values, were introduced. Implicit reasoning accuracy dipped considerably compared to explicit reasoning when these changes were applied, reinforcing the reliance on experience rather than systematic process.
3. Experimental Controls: By altering arithmetic problem presentations—via order reversal and division—the paper highlights differences in handling implicit and explicit task presentations, identifying that explicit reasoning maintains accuracy regardless of presentation changes.

Implications and Future Directions

The results emphasize that implicit reasoning is markedly different from explicit CoT reasoning. This suggests that current models do not intrinsically equate to systematic, step-by-step reasoning. For practical applications, this underlines a cautionary approach in tasks demanding high precision, where implicit reasoning might lead to oversight.

The future development of AI and LLMs may benefit from incorporating techniques that better emulate explicit CoT without significant computational overhead, possibly looking into hybrid modes that balance token outputs and reasoning steps. This research highlights ongoing challenges in achieving efficient, reliable implicit CoT reasoning and underscores the necessity of explicit methodologies for complex task solutions.

In conclusion, this paper provides critical insights into the contrast between implicit and explicit reasoning, advocating the continued utilization of Chain-of-Thought methodologies to ensure accuracy and reliability in LLM applications. These findings are a salient reminder of the nuanced complexities involved in enhancing the problem-solving capabilities of LLMs.