Chain-of-Thought Reasoning Without Prompting

Summary

The paper investigates the inherent capabilities of LLMs to generate CoT reasoning paths. This study introduces CoT-decoding, a method that explores alternative top-k tokens in the decoding space to uncover reasoning paths without any specialized prompting. The findings indicate that the presence of a CoT reasoning path correlates with increased model confidence in its final answer. The paper highlights that CoT-decoding can enhance the reasoning performance of language models by extracting more reliable decoding paths.

Strengths

1. The paper introduces CoT-decoding to elicit reasoning paths and provides an effective alternative to prompt engineering. 2. The results demonstrate significant improvements over greedy decoding, showcasing the practical benefits of the proposed method. 3. The study finds a clear correlation between the presence of CoT paths and increased model confidence, providing a useful metric for identifying reliable decoding paths.

Weaknesses

1. The primary contribution of the paper appears to be a method for selecting a decoding path from the top-k generated paths. While this approach is useful, it lacks novelty and significant impact. 2. The motivation behind the study is unclear to me for several reasons. Firstly, providing questions alone constitutes a form of prompting instead of no prompting. Secondly, the use of a prompt that inherently challenges the elicitation of CoT reasoning paths via greedy decoding, only to then introduce a specialized decoding method to address this, strikes me as contrived. Lastly, I question the claim that CoT-decoding effectively enhances the reasoning capabilities of language models, as it merely selects from pre-generated paths rather than uncovering new, substantive reasoning processes. 3. The proposed method may face challenges in tasks where identifying correct answer spans is difficult, limiting its practical applicability. 4. The comparison between CoT-decoding and greedy decoding is unfair. CoT-decoding inherently requires multiple reasoning paths to be generated first, which is not a requirement for greedy decoding. The exploration of multiple decoding paths significantly increases the computational cost, which could be a drawback for practical applications. 5. The evidence provided in the paper is restricted to a few reasoning benchmarks and toy tasks. To convincingly demonstrate its utility, the method should be tested across a broader array of datasets, such as the MATH benchmark, and in more complex real-world scenarios. 6. The range of models evaluated in the study is too narrow. Including mainstream models like GPT-3.5, GPT-4, and diverse open-source models like llama3 in various sizes would provide a more robust evaluation of the method's effectiveness.

Questions

NA

Rating

4

Confidence

4

Soundness

2

Presentation

3

Contribution

2

Limitations

Yes

Summary

The paper explores the inherent reasoning capabilities of large language models (LLMs) without the need for explicit prompting. By altering the decoding process to consider top-k alternative tokens, the authors reveal that Chain-of-Thought (CoT) reasoning paths can emerge naturally. This approach bypasses the need for task-specific prompt engineering and demonstrates that LLMs possess intrinsic reasoning abilities. Extensive empirical evaluations across various reasoning benchmarks show significant performance improvements using CoT-decoding compared to conventional greedy decoding.

Strengths

- The paper introduces a novel approach to reveal LLMs' reasoning capabilities without explicit prompts by altering the decoding process. - The paper is clearly written, with detailed explanations and illustrations of the CoT-decoding process. - The findings challenge the prevailing notion that LLMs require specific prompting for reasoning, highlighting the models' intrinsic abilities.

Weaknesses

- The approach, while novel in its specific application, builds on existing concepts of decoding and model confidence. The novelty is somewhat incremental. - The scope of experiments could be broadened to include more diverse and complex reasoning tasks. Additional benchmarks and comparisons with state-of-the-art prompting methods would strengthen the paper. - The paper could benefit from a more detailed comparative analysis with other decoding and prompting strategies to contextualize its contributions better.

Questions

- Can the authors provide more detailed comparisons with other state-of-the-art prompting and decoding methods? - How does the CoT-decoding method perform on more complex and diverse reasoning tasks not covered in the current experiments? - Are there any potential limitations or biases introduced by relying on top-k alternative tokens for decoding?

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

2

Limitations

The authors have addressed some limitations of their work, such as the additional computational costs incurred by exploring alternative decoding paths. However, the paper could further discuss the potential biases introduced by this approach and its applicability to more complex, open-ended tasks.

Summary

The paper investigates an innovative approach to eliciting chain-of-thought (CoT) reasoning from pre-trained large language models (LLMs) without the need for explicit prompting techniques, which typically require intensive prompt engineering and can obscure the model's inherent reasoning capabilities. Instead, this study proposes altering the decoding process by exploring alternative top-k tokens, rather than the conventional top-1 greedy decoding path. This method, termed CoT-decoding, effectively reveals natural CoT reasoning paths within the language model's outputs, leading to more accurate and confident model responses across various reasoning tasks. Contributions: 1. **Novel Decoding Strategy:** The paper introduces a novel decoding strategy that bypasses the need for prompt engineering by modifying the decoding process to explore alternative token paths, allowing the model to display its intrinsic reasoning capabilities. 2. **Empirical Validation:** Extensive empirical studies demonstrate that this CoT-decoding approach not only enhances the accuracy of LLMs on reasoning tasks but also increases the confidence of the models in their answers, suggesting a higher reliability of the reasoning paths discovered. 3. **Comparative Analysis:** The paper provides a comparative analysis of CoT-decoding against traditional methods, showing significant improvements over greedy decoding and other baseline methods across multiple benchmarks for mathematical and commonsense reasoning tasks.

Strengths

1. The paper introduces a novel approach to eliciting chain-of-thought (CoT) reasoning from pre-trained large language models (LLMs) without requiring explicit prompting. This method diverges from conventional prompting techniques, which involve either few-shot or zero-shot prompting, by modifying the decoding process to explore alternative token paths. This strategy is both innovative and creative as it challenges the standard practice of prompt engineering and demonstrates that CoT reasoning can be naturally elicited through decoding adjustments. 2. The paper is supported by extensive empirical studies that validate the efficacy of the CoT-decoding method. The experiments are well-designed, covering a broad range of reasoning tasks, including mathematical and commonsense reasoning benchmarks. 3. The paper is exceptionally clear and well-organized. The authors provide a detailed description of the CoT-decoding process, accompanied by illustrative figures and examples that help clarify how the method diverges from traditional decoding strategies.

Weaknesses

1. The method involves generating multiple decoding paths and evaluating them to identify the most confident reasoning trajectory, which could be computationally expensive, especially when applied at scale or in real-time applications. 2. The approach heavily relies on the selection of top-k alternative tokens, which may not always yield the most relevant or coherent paths for reasoning, especially in more complex or nuanced tasks.

Questions

See weekness.

Rating

8

Confidence

5

Soundness

3

Presentation

3

Contribution

3

Limitations

None. Good Paper~

Summary

The paper investigates the intrinsic reasoning capabilities of LLMs without relying on prompting techniques like few-shot or zero-shot prompting. The study introduces an alternative approach by altering the decoding process, specifically by exploring the top-k alternative tokens rather than following the standard greedy decoding path. The proposed "CoT-decoding," reveals that LLMs inherently possess chain-of-thought reasoning abilities that are often hidden by human-involved prompting / conventional decoding methods.

Strengths

- The paper demonstrates that LLMs can generate CoT reasoning paths without explicit prompts by modifying the decoding process. This challenges the prevailing notion that LLMs require prompting to reason effectively. It shows that LLMs have inherent reasoning capabilities that are more accurately assessed by CoT-decoding. - Experimental results show that CoT-decoding naturally reveals CoT reasoning paths during the decoding process, significantly enhancing the model's reasoning capabilities and surpassing greedy decoding. It is also observed that these paths are more prevalent in tasks frequently encountered in pre-training data. - The paper is generally well-written and easy to follow.

Weaknesses

- **Extra Computational Cost**: Exploring top-k alternative tokens for decoding paths requires additional computational resources. Compared to CoT/Zero-shot CoT/ComplexCoT/CoT-SC(n) where n is not a large number, CoT-decoding necessitates evaluating multiple decoding paths, which increases computational complexity and time cost, especially when dealing with complex multi-step tasks. - **Limitations in Open-ended Questions**: This method mainly relies on the model's confidence in specific answers during the decoding process to select reasoning paths. However, for more open-ended questions, this probability difference-based selection method may not be precise enough, making it challenging for LLMs to select the optimal path in a much larger answer space.

Questions

Does the limitation of branching only at early decoding stages restrict the flexibility and applicability of CoT-decoding? Would exploring branching at later stages in the decoding process improve overall performance?

Rating

8

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

I do not identify any negative societal impacts in this paper.

Official Comment by Reviewer BrnW

Thank you for your thorough rebuttal and for addressing the concerns raised. I believe this work is very meaningful and provides valuable insights into enhancing the model's reasoning capabilities and constructing higher-quality datasets. In light of your detailed response and the efforts made to address these complexities, I have decided to increase my score from 7 to 8.