Original Paper: https://arxiv.org/abs/2305.09993
By: Weijia Xu, Andrzej Banburski-Fahey, Nebojsa Jojic
Abstract:
We introduce Reprompting, an iterative sampling algorithm that searches for the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, we infer CoT recipes that work consistently well for a set of training samples. Our method iteratively samples new recipes using previously sampled solutions as parent prompts to solve other training problems. On five Big-Bench Hard tasks that require multi-step reasoning, Reprompting achieves consistently better performance than the zero-shot, few-shot, and human-written CoT baselines. Reprompting can also facilitate transfer of knowledge from a stronger model to a weaker model leading to substantially improved performance of the weaker model. Overall, Reprompting brings up to +17 point improvements over the previous state-of-the-art method that uses human-written CoT prompts.
Summary Notes
Simplifying Reprompting with Gibbs Sampling for Better AI Reasoning
The field of artificial intelligence (AI), especially in language models, is constantly seeking better ways to improve multi-step reasoning.
Traditional methods sometimes struggle with complex tasks, sparking innovation in this area. A new method called Reprompting is changing how we automate Chain-of-Thought (CoT) prompt generation, using a technique known as Gibbs sampling.
Enhancing Chain-of-Thought Prompting
In-context learning, where models learn from examples within a prompt, is foundational to Chain-of-Thought prompting. This method aims to clearly and deeply guide models through complex reasoning tasks.
Key Points of In-Context Learning:
- Uses direct examples in prompts for model learning.
- Ensures output consistency, no matter the example order or choice.
Improving Language Model Performance with CoT:
- CoT prompting helps detail every step in a reasoning process, improving how language models tackle complex tasks.
Reprompting and Gibbs Sampling
Reprompting refines CoT prompts in an iterative manner using Gibbs sampling. This allows for precise and flexible exploration of solutions.
How Reprompting Works:
- Core Technique: Uses Gibbs sampling for iterative prompt refinement, enhancing reasoning and accuracy over time.
- Automated Prompt Generation: Begins with basic solutions, then continually improves CoT prompts automatically.
- Enhancing Multiple Models: Works across different language models, improving prompt generation collaboratively.
Testing and Results
Reprompting was tested on five challenging tasks, outperforming traditional methods like zero-shot, few-shot, and manual CoT prompts.
Key Findings:
- Outperformed Baselines: Showed superior performance in complex reasoning tasks.
- Optimized for Different Models: Demonstrated its adaptability to various model architectures.
Comparing with Previous Work
Reprompting builds on existing work in in-context learning and automated prompt generation but sets a new standard for efficiency and scalability.
Advancements Over Previous Methods:
- Offers significant improvements in automated CoT prompt generation, requiring less human intervention and offering more sophisticated automation.
Looking Forward: Advancing AI Reasoning
Reprompting marks a significant step in automated CoT prompting, enhancing language models' ability to tackle complex reasoning tasks. It opens new avenues for understanding and improving AI reasoning.
Implications for AI Development:
- Represents a leap forward in enabling sophisticated, autonomous reasoning in AI systems, promising more advanced machine understanding of complex thought processes.
Access to Resources:
- The methodology and results are available on GitHub, encouraging further exploration and innovation in the AI community.
Reprompting isn't just a new method; it's a milestone in AI research, revolutionizing the generation of Chain-of-Thought prompts and paving the way for future advancements in artificial intelligence reasoning.
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