Instance-adaptive Zero-shot Chain-of-Thought Prompting (2409.20441v3)

Abstract: Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of LLMs in real-world reasoning tasks. Nonetheless, the efficacy of a singular, task-level prompt uniformly applied across the whole of instances is inherently limited since one prompt cannot be a good partner for all, a more appropriate approach should consider the interaction between the prompt and each instance meticulously. This work introduces an instance-adaptive prompting algorithm as an alternative zero-shot CoT reasoning scheme by adaptively differentiating good and bad prompts. Concretely, we first employ analysis on LLMs through the lens of information flow to detect the mechanism under zero-shot CoT reasoning, in which we discover that information flows from question to prompt and question to rationale jointly influence the reasoning results most. We notice that a better zero-shot CoT reasoning needs the prompt to obtain semantic information from the question then the rationale aggregates sufficient information from the question directly and via the prompt indirectly. On the contrary, lacking any of those would probably lead to a bad one. Stem from that, we further propose an instance-adaptive prompting strategy (IAP) for zero-shot CoT reasoning. Experiments conducted with LLaMA-2, LLaMA-3, and Qwen on math, logic, and commonsense reasoning tasks (e.g., GSM8K, MMLU, Causal Judgement) obtain consistent improvement, demonstrating that the instance-adaptive zero-shot CoT prompting performs better than other task-level methods with some curated prompts or sophisticated procedures, showing the significance of our findings in the zero-shot CoT reasoning mechanism.

• The paper presents Instance-adaptive Prompting (IAP), a novel zero-shot CoT method that dynamically selects prompts for individual instances to enhance LLM reasoning.
• The IAP methods achieved 2%-4% higher accuracy than optimal task-level prompts on reasoning tasks like GSM8K and CommonsenseQA using various LLMs.
• This research offers a practical framework enhancing LLM reasoning robustness across diverse applications and provides valuable insights into the mechanisms of CoT.

Instance-Adaptive Zero-Shot Chain-of-Thought Prompting

This paper presents a compelling advancement in the field of zero-shot chain-of-thought (CoT) prompting for LLMs. The authors introduce an instance-adaptive prompting algorithm designed to enhance the reasoning capabilities of LLMs across a variety of tasks without relying on task-specific prompts.

Key Contributions

The primary contribution of this work lies in the methodological innovation of instance-adaptive prompting (IAP). Unlike traditional approaches that apply a uniform prompt across all instances of a task, this research proposes a strategy that dynamically selects prompts at the instance level. The authors argue that a singular task-level prompt cannot accommodate the diversity of instances adequately, a claim which the paper supports with empirical evidence.

Methodology

The authors analyze information flow during zero-shot CoT reasoning through the computation of saliency scores. These scores measure the semantic interaction between three key components: the question, the prompt, and the rationale. The analysis reveals that effective reasoning is characterized by the prompt's ability to harness semantic information from the question, facilitating a comprehensive rationale built from this enriched context. Conversely, a failure in capturing this information flow often results in poor reasoning outcomes.

The proposed IAP strategy consists of two methods: Sequential Substitution (IAP-ss) and Majority Vote (IAP-mv). IAP-ss incrementally evaluates prompts against a threshold to identify effective ones promptly, emphasizing efficiency. On the other hand, IAP-mv calculates synthesized saliency scores across prompts, using a majority vote to determine the final answer, thereby enhancing robustness at the cost of efficiency.

Results

Extensive experiments benchmark the IAP against existing zero-shot CoT methods using various LLMs, including LLaMA-2, LLaMA-3, and Qwen, across a range of reasoning tasks such as GSM8K and CommonsenseQA. The results consistently demonstrate IAP's superiority, showcasing a 2%-4% improvement in accuracy over optimal task-level prompts. Notably, the paper also discusses the computational trade-offs between the IAP-ss and IAP-mv methods, with the latter achieving marginally higher performance at the expense of increased computational cost.

Implications and Future Directions

The instance-adaptive approach presented in this research has significant implications for the design of future LLM prompting strategies. By focusing on the dynamic selection of prompts, IAP achieves a balance between efficiency and accuracy that static task-level prompts lack. The findings highlight the nuanced nature of effective reasoning in LLMs and point to the potential of individualized prompting strategies tailored to specific instances.

Theoretically, this work enhances our understanding of how LLMs process semantic information during CoT reasoning, offering insights that can inform both the development of more sophisticated models and the refinement of existing ones. Practically, the IAP can be integrated into various applications requiring robust reasoning capabilities, providing a framework that can adapt to the demands of diverse contexts without extensive retraining or fine-tuning.

Future developments may explore the further refinement of saliency score analysis to gain deeper insights into the underlying mechanisms of LLM reasoning. Additionally, extending the instance-adaptive framework to other types of reasoning tasks or integrating it with few-shot learning paradigms could offer exciting avenues for improving AI's cognitive functionalities.

In conclusion, this paper makes a substantial contribution by challenging the conventional task-level prompting paradigm and advocating for a flexible, instance-focused approach that reveals the latent potential of LLMs in zero-shot CoT reasoning tasks.