Unlocking Expertise with Language Models: An Exploration using Expert Prompting Techniques with Examples for ML, AI and CS Questions

A recently  prompting approach proposes to ask LLMs to respond as an expert. It involves 3 different steps:

  • Ask LLM to identify experts in a given field related to the prompt/question
  • Ask LLM to respond to the question as if it was each of the experts
  • Make final decision as a collaboration between the generated responses.

Let's break down the three-step process outlined above and apply it to a few subjects like Computer Science, Machine Learning, Information Science, and Artificial Intelligence.

Example 1: Computer Science

Question: "What is the significance of quantum computing?"

Step 1: Identifying experts. Some experts in the field of Quantum Computing include:

  1. Peter Shor - Known for Shor's algorithm in quantum computing.
  2. John Martinis - A professor of physics who has done extensive work in quantum computing.
  3. Michelle Simmons - A leading researcher in quantum physics and technology.

Step 2: Generating responses as if I were each expert.

As Peter Shor: "Quantum computing holds immense significance because it can fundamentally change our computational abilities. It could make certain problems that are virtually impossible to solve on classical computers tractable, thanks to the algorithm I developed."

As John Martinis: "Quantum computing is pivotal because it offers an entirely new way of manipulating information. The principles of quantum mechanics could be used to develop ultra-efficient algorithms and to boost computational power in a way that classical systems could never match."

As Michelle Simmons: "Quantum computing, by harnessing the power of quantum bits (qubits), could revolutionize fields from cryptography to material science. These machines have the potential to perform complex calculations at a speed that would leave even the most advanced classical supercomputers behind."

Step 3: Final decision. Given the expertise of all these individuals, we can see that the significance of quantum computing lies in its potential to solve complex problems efficiently, leverage quantum mechanics for information processing, and revolutionize various fields like cryptography and material science.

Example 2: Machine Learning

Question: "What is the potential impact of deep learning on healthcare?"

Step 1: Identifying experts. Some experts in the field of Deep Learning in Healthcare include:

  1. Geoffrey Hinton - A pioneer in the field of deep learning.
  2. Andrew Ng - Founder of Google Brain and a leader in the AI and ML community.
  3. Regina Barzilay - A professor at MIT who uses machine learning for oncology and drug discovery.

Step 2: Generating responses as if I were each expert.

As Geoffrey Hinton: "Deep learning has the potential to transform healthcare in numerous ways, from automating medical imaging and diagnosis to predicting patient outcomes based on a complex web of factors."

As Andrew Ng: "Deep learning algorithms can process vast amounts of data and detect patterns that humans can't. In healthcare, this could mean early disease detection, personalized treatments, and ultimately, improved patient outcomes."

As Regina Barzilay: "Deep learning can significantly accelerate drug discovery and predict the course of diseases like cancer. It's an exciting time as we integrate AI into healthcare to revolutionize patient care and treatment."

Step 3: Final decision. From these expert perspectives, it's clear that deep learning has the potential to transform healthcare through automation, early disease detection, personalized treatment, and accelerated drug discovery.

Example 3: Information Science

Question: "How can big data impact business decision-making?"

Step 1: Identifying experts. Some experts in the field of Big Data and Business include:

  1. Viktor Mayer-Schönberger - Author of "Big Data: A Revolution That Will Transform How We Live, Work, and Think".
  2. Bernard Marr - A strategic performance consultant specializing in data analysis and business decision-making.
  3. Jennifer Priestley - A professor of Statistics and Data Science, known for her work in big data.

Step 2: Generating responses as if I were each expert.

As Viktor Mayer-Schönberger: "Big data can revolutionize business decision-making by providing comprehensive insights that weren't possible before. It can uncover trends and patterns that can be leveraged for strategic planning and decision-making."

As Bernard Marr: "Big data allows businesses to make decisions based on data, not intuition. It provides detailed insights into customer behavior, market trends, and operational efficiency, helping businesses make informed, evidence-based decisions."

As Jennifer Priestley: "Big data gives businesses the power to predict future trends and behaviors. It allows for more accurate forecasting and strategic planning, which can significantly impact a company's success."

Step 3: Final decision. From these expert perspectives, big data can greatly impact business decision-making by providing comprehensive insights, supporting evidence-based decisions, and enabling accurate forecasting and strategic planning.

Example 4: Artificial Intelligence

Question: "What ethical challenges does artificial intelligence pose?"

Step 1: Identifying experts. Some experts in the field of AI Ethics include:

  1. Timnit Gebru - A researcher known for her work on algorithmic bias and the ethical implications of AI.
  2. Wendell Wallach - Scholar at Yale University's Interdisciplinary Center for Bioethics and author of "Moral Machines".
  3. Kate Crawford - Senior Principal Researcher at Microsoft Research and author of "The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence".

Step 2: Generating responses as if I were each expert.

As Timnit Gebru: "AI presents ethical challenges around bias and fairness. If AI systems are trained on biased data, they will produce biased outcomes, perpetuating existing inequalities."

As Wendell Wallach: "AI systems raise significant ethical questions about responsibility. As these systems take over tasks previously done by humans, it's crucial to establish who is accountable when things go wrong."

As Kate Crawford: "AI can have wide-ranging ethical impacts, from labor displacement to environmental costs. We need to consider these factors when deploying AI and strive for equitable, sustainable AI practices."

Step 3: Final decision. The ethical challenges posed by AI include addressing issues of bias and fairness, establishing accountability, and considering the social and environmental impacts of AI technology.

In these examples, each question is answered by referencing multiple expert opinions and then drawing a conclusion based on the synthesized information. This approach offers a comprehensive perspective on complex subjects and can be very useful in an educational setting.