I was looking into hallucination-reducing techniques for prompting and came across “Chain of Verification” pattern. This blog post goes into an example of the “Chain of Verification” pattern.
https://www.youtube.com/watch?v=Lar3K2gN454&t
The Prompt
The actual prompt for this post is provided below. You can copy / paste it, and give it a try yourself. I will break down the prompt into it’s parts below.
I will give you a topic “TOPIC”, please summarize it . Your summary should be 3–5 paragraphs long and include only factual information that can be directly sourced from the Encyclopaedia Britannica. Now generate “N” questions that would verify the accuracy and completeness of your summary of the French Revolution. Then, answer the verification questions you generated to confirm the reliability of your summary. As part of answering the questions, please also provide brief reasoning for the answers. Then, based on your answers to the verification questions, please revise your initial summary to address any gaps or inaccuracies. Then review your revised summary. Does it satisfactorily address all the verification questions you generated? If so, provide the final version of your summary. Also, call out the main differences between the final summary and the initial summary. Ask me for the topic, and then follow the instructions as called out above.
Prompt Breakdown
Let’s break the prompt into parts.
Generate Initial Content
We parameterized the “TOPIC” so that we can re-use this prompt in many different situations.
I will give you a topic “TOPIC”, please summarize it . Your summary should be 3–5 paragraphs long and include only factual information that can be directly sourced from the Encyclopaedia Britannica.
Generate validation questions for the above
We parameterized the number of questions — “N” so that we can be flexible about how much verification we need. For simpler topics, N can be smaller, for more complex topics, N can be larger.
Now generate “N” questions that would verify the accuracy and completeness of your summary of the French Revolution.
Answer and justify your answers to validation questions
Then, answer the verification questions you generated to confirm the reliability of your summary. As part of answering the questions, please also provide brief reasoning for the answers.
Revise your summary
Then, based on your answers to the verification questions, please revise your initial summary to address any gaps or inaccuracies.
Confirm all initial verifications have been answered properly
Does it satisfactorily address all the verification questions you generated? If so, provide the final version of your summary.
Generate a diff for quick final manual validation
Also, call out the main differences between the final summary and the initial summary.
Ask user for inputs related to “TOPIC” and “N”
Ask me for the topic, and then follow the instructions as called out above.
Prompt Usage
I entered the above prompt into ChatGPT, and gave it the following inputs
Why did deep learning not become popular before 2012, 10 verification questions.
Below is the output from ChatGPT.
Deep learning, as a subset of artificial intelligence (AI), had been around for decades before its rise to prominence in 2012. However, several factors contributed to its limited popularity prior to that time:
Limited computational power: Deep learning models require significant computational resources to train effectively, especially with large datasets. Before 2012, the computational power needed for training deep neural networks was often prohibitively expensive and not widely accessible.
Sparse data: Deep learning algorithms thrive on large volumes of data to learn complex patterns and relationships. Prior to 2012, the availability of such vast datasets, particularly labeled data, was more limited compared to recent years. This scarcity hindered the training and performance of deep learning models.
Lack of breakthroughs: While the theoretical foundations of deep learning were established in the 1980s and 1990s, there were no major breakthroughs or significant advancements in algorithm design or training techniques until around 2012. These breakthroughs, such as the development of deep convolutional neural networks and improved training methods like backpropagation, played a crucial role in the resurgence of deep learning.
Limited awareness and interest: Before 2012, deep learning was not as widely known or discussed outside of academic circles and specialized research communities. The lack of awareness and interest in the potential applications of deep learning further contributed to its limited popularity.
Performance limitations: Early deep learning models often struggled to outperform traditional machine learning approaches on various tasks. This limited success hindered widespread adoption and investment in deep learning research and development.
In summary, the limited availability of computational resources, sparse data, lack of breakthroughs, limited awareness, and performance limitations were key factors that prevented deep learning from becoming popular before 2012. It wasn’t until advancements in computational technology, algorithm design, and data availability that deep learning gained widespread attention and adoption in the years following 2012.
Next up are the verification questions
What were some of the factors that limited the popularity of deep learning before 2012?
How did the computational requirements for training deep neural networks contribute to the limited popularity of deep learning before 2012?
What role did the availability of large datasets play in hindering the development of deep learning before 2012?
Can you explain why breakthroughs in algorithm design and training techniques were crucial for the resurgence of deep learning around 2012?
How did the lack of awareness and interest in deep learning impact its popularity before 2012?
What challenges did early deep learning models face in outperforming traditional machine learning approaches?
Can you elaborate on the theoretical foundations of deep learning and when they were established?
What were some of the limitations of computational technology that affected the popularity of deep learning before 2012?
How did the performance limitations of early deep learning models influence their adoption and investment?
What were some of the key advancements in computational technology and algorithm design that contributed to the rise of deep learning after 2012?
Then, answers to the verification questions
What were some of the factors that limited the popularity of deep learning before 2012? Answer: Before 2012, factors such as limited computational power, sparse data availability, lack of breakthroughs in algorithm design and training techniques, limited awareness and interest, and performance limitations hindered the popularity of deep learning.
How did the computational requirements for training deep neural networks contribute to the limited popularity of deep learning before 2012? Answer: Deep neural networks require significant computational resources for training, which were often expensive and not widely accessible before 2012. This limitation restricted the widespread adoption and development of deep learning models.
What role did the availability of large datasets play in hindering the development of deep learning before 2012? Answer: Deep learning algorithms thrive on large volumes of data to learn complex patterns and relationships. However, before 2012, the availability of such vast datasets, especially labeled data, was limited, hindering the development and training of deep learning models.
Can you explain why breakthroughs in algorithm design and training techniques were crucial for the resurgence of deep learning around 2012? Answer: Breakthroughs in algorithm design, such as the development of deep convolutional neural networks, and improved training techniques like backpropagation, were crucial for the resurgence of deep learning around 2012. These advancements significantly improved the performance and effectiveness of deep learning models.
How did the lack of awareness and interest in deep learning impact its popularity before 2012? Answer: Before 2012, deep learning was not widely known or discussed outside of academic circles and specialized research communities. The lack of awareness and interest in the potential applications of deep learning further contributed to its limited popularity.
What challenges did early deep learning models face in outperforming traditional machine learning approaches? Answer: Early deep learning models often struggled to outperform traditional machine learning approaches on various tasks due to performance limitations and the lack of breakthroughs in algorithm design and training techniques.
Can you elaborate on the theoretical foundations of deep learning and when they were established? Answer: The theoretical foundations of deep learning, such as neural network architectures and learning algorithms, were established in the 1980s and 1990s. However, it wasn’t until advancements in computational technology and algorithm design in the early 2010s that deep learning experienced a resurgence in popularity.
What were some of the limitations of computational technology that affected the popularity of deep learning before 2012? Answer: Before 2012, computational technology had limitations in terms of processing power and memory capacity, which made training deep neural networks prohibitively expensive and resource-intensive.
How did the performance limitations of early deep learning models influence their adoption and investment? Answer: The performance limitations of early deep learning models, coupled with the lack of significant breakthroughs, influenced limited adoption and investment in deep learning research and development before 2012.
What were some of the key advancements in computational technology and algorithm design that contributed to the rise of deep learning after 2012? Answer: After 2012, advancements in computational technology, such as the availability of graphics processing units (GPUs) and cloud computing, along with breakthroughs in algorithm design and training techniques, contributed to the rise of deep learning. These advancements enabled more efficient training of deep neural networks on large datasets.
Then we have the revised summary
Revised Summary:
Deep learning, a subset of artificial intelligence (AI), experienced limited popularity before 2012 due to several factors. Firstly, the computational power required for training deep neural networks was often prohibitively expensive and not widely accessible. Additionally, the availability of large datasets, essential for deep learning algorithms to learn complex patterns, was limited before 2012, hindering the development and training of deep learning models. Breakthroughs in algorithm design and training techniques, such as the development of deep convolutional neural networks and improved backpropagation methods, were crucial for the resurgence of deep learning around 2012.
Before 2012, deep learning was not widely known or discussed outside of academic circles and specialized research communities, contributing to limited awareness and interest in its potential applications. Early deep learning models also faced performance limitations, struggling to outperform traditional machine learning approaches on various tasks. Despite the theoretical foundations of deep learning being established in the 1980s and 1990s, significant advancements in computational technology and algorithm design were necessary for its widespread adoption.
The limitations of computational technology, including processing power and memory capacity, affected the popularity of deep learning before 2012. Additionally, the lack of significant breakthroughs and performance limitations of early deep learning models influenced limited adoption and investment in deep learning research and development. However, after 2012, advancements in computational technology, such as the availability of graphics processing units (GPUs) and cloud computing, along with breakthroughs in algorithm design and training techniques, contributed to the rise of deep learning. These advancements enabled more efficient training of deep neural networks on large datasets.
Lastly, we have a diff of the different summaries for quick and final manual verification.
Main Differences: The revised summary incorporates more detailed explanations of the factors that limited the popularity of deep learning before 2012, including computational requirements, data availability, lack of breakthroughs, awareness, and performance limitations. It also highlights the importance of advancements in computational technology and algorithm design in the resurgence of deep learning after 2012. Overall, the revised summary provides a more comprehensive understanding of the evolution of deep learning.
References
- The YouTube video “Chain-of-Verification (COVE) Reduces Hallucination in Language Models” -> https://www.youtube.com/watch?v=Lar3K2gN454&t
- The arXiv paper “Pattern-Aware Chain-of-Thought Prompting in Large Language Models” 5 discusses extending the COVE approach by incorporating pattern-aware prompting to improve reasoning performance.
- Large Language Models are Better Reasoners with Self-Verification https://arxiv.org/abs/2212.09561
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