Automating the Chain of Thought: How AI Can Prompt Itself to Reason
Auto-CoT prompting method has LLMs automatically generate their own demonstrations to prompt complex reasoning, using diversity-based sampling and zero-shot generation, reducing human effort in creating prompts. Experiments show it matches performance of manual prompting across reasoning tasks.
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- Chain-of-thought (CoT) prompting improves LM reasoning by providing step-by-step examples
- Manual creation of CoT demonstrations requires non-trivial human effort
- This paper explores automating CoT demonstration generation using the LM itself
- The proposed Auto-CoT method clusters questions then samples diverse ones for self-prompting
- Experiments show Auto-CoT matches manually created CoT, without human involvement
The paper "Automatic Chain of Thought Prompting in Large Language Models" explores automated ways to create effective "chain of thought" (CoT) prompts for large language models (LLMs) like GPT-4. CoT prompting involves showing the LLM examples that demonstrate step-by-step reasoning chains mapping from a question to a final answer. This improves performance on complex reasoning tasks.
The best CoT prompting results, however, currently require humans to manually create demonstrations, with hand-crafted questions and detailed reasoning steps tailored to each task. The authors propose eliminating this manual effort by having the LLM automatically generate its own CoT demonstrations for prompting. Their key method, called Auto-CoT, works by first clustering the questions of a given task based on their semantic similarity. Auto-CoT then samples a diverse set of questions covering different clusters. For each sampled question, Auto-CoT uses the LLM itself in zero-shot mode to produce a reasoning chain from the question to an answer. It applies simple heuristics to select chains based on length and simplicity.
The authors perform experiments evaluating Auto-CoT on 10 reasoning datasets spanning arithmetic, common sense, and symbolic logic problems. The results show that Auto-CoT matches or exceeds the performance of CoT prompting based on manually created demonstrations, without requiring any human effort to design demonstrations. A key insight is that using diversity-based sampling over similarity-based retrieval to select the prompting questions mitigates the impact of imperfect demonstrations generated by the LLM's zero-shot reasoning. Auto-CoT also substantially outperforms baselines like retrieving similar questions or random sampling for the demonstrations.
Overall, the work provides strong evidence that LLMs can prompt themselves to demonstrate complex multi-step reasoning. Auto-CoT essentially composes one LLM that generates a diverse set of CoT examples, with another LLM that uses those examples for inference. The authors suggest this self-prompting approach could significantly extend prompting techniques and make LLMs much better few-shot learners on complex reasoning tasks. Limitations include potential computational costs and issues scaling to more unconstrained problems. But the ability to automate prompting reduces human effort and customization needs.
How does Auto-CoT compare to other methods that automate prompt creation, like retrieval-augmented prompting?
Retrieval-augmented prompting retrieves related data examples to use for prompting, rather than having the LLM generate demonstrations. A key difference is that Auto-CoT doesn't require a dataset of labeled examples and instead relies on the LLM's own zero-shot reasoning. Retrieval may be more sample-efficient but requires data collection. Auto-CoT is fully automated but can suffer from imperfect demonstrations.
Could Auto-CoT be applied to natural language generation tasks beyond logical reasoning?
The clustering and self-prompting approach seems promising for less structured textual tasks where coherence is important. For example, Auto-CoT could provide writing planning examples for creative writing, or dialog illustrations for conversational bots. The key challenges would be defining appropriate clustering methods and training the LLM's zero-shot generation for high-quality demonstrations.
What is innovative about this research?
The key innovation is using the LLM itself to generate demonstrations for prompting, instead of relying on manual creation. This allows prompting to become more automated and task-adaptive. The clustering to select diverse questions for self-prompting is also innovative.
What are the broader implications of this research?
This research could significantly reduce the human effort and expertise needed to design effective prompts. It may allow LLMs to learn new tasks more quickly and from less data, enhancing their few-shot learning capabilities. The self-prompting approach could be applied to extend prompting techniques like in-context learning.
What are some potential issues or oversights with this research as presented, if any?
A potential issue is that Auto-CoT relies on clustering questions based on similarity features from Sentence-BERT. Performance could suffer on tasks where semantic similarity doesn't align well with reasoning similarity. The approach also likely incurs higher compute costs than standard prompting.
What are the logical next research steps from this research?
Important next steps include exploring how Auto-CoT scales to more complex and open-ended reasoning tasks, integrating it with retrieval of external knowledge sources, and studying if the approach can be learned more sample-efficiently through meta-learning rather than relying solely on a pre-trained LLM. Analyzing the interplay between cluster count, sample size, and performance is also an open question.
- Auto-CoT reduces the need for hand-crafted demonstrations to prompt LMs
- Self-prompting with Auto-CoT composes one LM generating diverse examples, and another inferring
- Diversity in sampling questions is key to overcoming imperfect zero-shot reasoning chains
- The approach could extend prompting techniques and make LMs better few-shot learners
- Auto-CoT demonstrates the promise of automating prompting to reduce human effort
- Next steps include scaling Auto-CoT to more complex reasoning tasks and larger LMs
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.