(Gemini)
Self-consistency is a technique used in large language models to improve the reliability of their answers, especially for complex reasoning tasks. It works by having the model generate multiple, diverse reasoning paths to a single problem and then selecting the most frequently occurring or consistent answer from those paths.
Chain of Thought with Self-Consistency is a method used in large language models (LLMs) to improve the accuracy and reliability of their answers.
It involves two main steps: Chain of Thought (CoT), where the model generates a step-by-step reasoning process, and Self-Consistency, where the model repeats this process multiple times and then selects the most common or consistent answer. This approach is particularly effective for complex problems that require multi-step reasoning.
10 Examples of CoT with Self-Consistency
Medical Diagnosis: A doctor's AI assistant is asked to diagnose a patient based on symptoms.
CoT: The AI generates a reasoning path: "Patient has fever, cough, and fatigue. These are common symptoms for flu, bronchitis, and pneumonia. Chest X-ray shows no consolidation, ruling out typical pneumonia. A rapid flu test is positive. Therefore, the diagnosis is influenza."
Self-Consistency: The AI runs this reasoning process five times, each time arriving at "influenza." Because this is the most frequent result, it's chosen as the final answer.
Legal Case Analysis: An AI is tasked with determining the most likely outcome of a court case.
CoT: The AI breaks down the case: "Plaintiff presented evidence A, B, and C. The defense rebutted with D and E. Witness testimony F supports the plaintiff. Precedent G applies to this case, favoring the plaintiff. The court is likely to rule in favor of the plaintiff."
Self-Consistency: The AI repeats this analysis three times, each time concluding the plaintiff will win. The high consistency makes the conclusion more reliable.
Creative Writing: An AI is asked to write a short story with a specific twist ending.
CoT: The AI plans the story: "Character A needs a secret. The secret must be revealed at the end. I'll build suspense by having them act suspiciously. The twist will be that the secret is something mundane, like a surprise party, not something sinister."
Self-Consistency: The AI drafts several versions of the story, with different details but all with the same twist ending. It then selects the version with the strongest narrative flow and most effective reveal.
Scientific Hypothesis Generation: An AI is asked to propose a new hypothesis for a biological phenomenon.
CoT: The AI considers known facts: "Enzyme X binds to substrate Y. The reaction rate increases with temperature up to a certain point. The enzyme's structure is a helix. A potential hypothesis is that the high temperature causes a conformational change in the enzyme's active site, increasing its affinity for the substrate."
Self-Consistency: The AI generates multiple hypotheses, all of which center on the idea of conformational changes. The consistency across these different paths strengthens the proposed hypothesis.
Historical Analysis: An AI analyzes the causes of a historical event.
CoT: The AI follows a causal chain: "Economic depression in the 1930s led to social unrest. The rise of extremist political parties capitalized on this unrest. This led to aggressive foreign policies and eventually, war."
Self-Consistency: The AI generates this causal chain from three different perspectives (economic, political, and social). All three paths converge on the same conclusion about the outbreak of the war, making the analysis more robust.
Software Debugging: An AI helps a programmer find a bug in their code.
CoT: The AI traces the code execution: "The function 'calculate_total' receives a floating-point number. It then casts it to an integer. The final result is incorrect because of this loss of precision. The bug is in the data type conversion."
Self-Consistency: The AI runs the same logical trace on three different test cases, each with varying input data. All traces identify the same line of code and the same bug, confirming the diagnosis.
Financial Investment Recommendation: An AI is asked to recommend a stock for investment.
CoT: The AI analyzes the company: "Company Z has strong revenue growth. Its P/E ratio is favorable compared to competitors. It recently launched a new product line with positive reviews. The technical analysis shows an upward trend. This is a good investment."
Self-Consistency: The AI runs this analysis using different financial models and data sources. If all models consistently recommend the stock, the confidence in the recommendation increases.
Urban Planning: An AI is asked to suggest a location for a new park.
CoT: The AI considers factors: "The area needs to be accessible by public transport. It should be in a residential zone. It must be far from industrial noise. A vacant lot near the intersection of Elm and Oak streets fits all these criteria."
Self-Consistency: The AI performs this same multi-factor analysis on several different candidate locations. The location that consistently scores highest across all these runs is chosen as the optimal one.
Recipe Creation: An AI is asked to create a recipe for a cake that is both gluten-free and vegan.
CoT: The AI plans the recipe: "Need a gluten-free flour blend. Use a plant-based milk and egg substitute. The leavening agent must work with these substitutes. A combination of baking soda and vinegar can provide the lift. Add cocoa powder for flavor."
Self-Consistency: The AI generates three slightly different versions of the recipe, each with minor variations (e.g., using different plant-based milks). The core steps and principles remain the same, ensuring a reliable final recipe.
Game Strategy: An AI is asked to devise a winning strategy for a complex board game.
CoT: The AI simulates the game: "If I move piece A here, my opponent will likely respond by moving piece B. This will expose their king. I can then move piece C to checkmate them."
Self-Consistency: The AI runs this simulation dozens of times, exploring different opening moves and opponent responses. The strategy that most consistently leads to a win, regardless of minor variations in the game, is selected as the optimal one.
Is Self-Consistency the same as in ML ensemble learning.
No, Self-Consistency isn't the same as traditional ML ensemble learning, though it's conceptually similar.
Key Differences
Self-Consistency: This technique is a decoding strategy for a single language model. It works by generating multiple "chains of thought" or reasoning paths for a given prompt by introducing a bit of randomness. It then takes a majority vote on the final answer to select the most consistent and likely correct result.
Think of it as a single expert "thinking" a problem through from several different angles and then choosing the answer that appears most often. The "ensemble" is created from the diverse outputs of a single model.
Ensemble Learning: This is a machine learning paradigm that combines the predictions from multiple, distinct models to get a more accurate and robust prediction than any single model could achieve on its own. For example, a random forest uses an ensemble of decision trees, and a voting classifier combines the outputs of different algorithms like a support vector machine, a neural network, and a logistic regression model.
The Analogy
A good analogy is a committee.
Ensemble learning is like asking for an opinion from multiple different experts—a doctor, a lawyer, a scientist, and a historian - on a complex societal issue and then combining their answers to get a well-rounded view.
Self-Consistency is like asking a single expert (e.g., a scientist) to write down several different ways they could solve a problem, and then picking the solution that they arrive at most often.
Please repeat the above answer showing to me chain of thought details
To provide a detailed "chain of thought" for the urban planning example, I'll walk through the internal reasoning process of an AI using a specific example.
Chain of Thought: A New Park for Sunnyside Neighborhood
Initial Prompt: "Suggest the best location for a new park in the Sunnyside neighborhood of Metropolis. The goal is to maximize community benefit."
Step 1: Deconstruct the Request
Goal: Suggest best location for a new park.
Location: Sunnyside neighborhood, Metropolis.
Primary Metric: Maximize community benefit.
Implied Task: I need to analyze data to define and measure "community benefit."
Step 2: Define "Community Benefit" (via Data Analysis)
Community benefit is not a single number. I'll break it down into quantifiable factors.
Factor A: Lack of Green Space. I need to identify areas within Sunnyside that are underserved by existing parks.
Data Check: I'll query my database for the locations of all existing parks in Metropolis.
Calculation: I'll run a spatial analysis to identify areas more than a 10-minute walk (approx. 0.5 miles) from any existing park.
Result: A map showing "green space gaps."
Factor B: High Population Density. The park should serve the most people.
Data Check: I'll query my database for Sunnyside's population density data, block by block.
Calculation: I'll identify census tracts with a high number of residents per square mile.
Result: A heat map of population density.
Factor C: Public Transit Accessibility. People should be able to get there without a car.
Data Check: I'll query my database for bus stops, subway stations, and bike lanes in Sunnyside.
Calculation: I'll identify potential sites within a 5-minute walk of a major public transit hub.
Result: A map showing public transit access points.
Factor D: Zoning and Availability. A park can only be built on land zoned for it.
Data Check: I'll query the city's zoning and property ownership records.
Calculation: I'll filter for parcels of land that are unowned, public, or zoned for recreational/public use. I'll also check for any historical or environmental constraints.
Result: A list of available, buildable parcels.
Step 3: Synthesize and Score Potential Locations
Now I'll use my chain of thought to evaluate the most promising parcels. I'll take three top contenders as examples: Parcel 1 (Elm Street Lot), Parcel 2 (Old Rail Yard), and Parcel 3 (Pine Street Field).
Parcel 1: Elm Street Lot
Location: Center of Sunnyside.
Analysis:
Green Space Gap: Yes, this area is 0.7 miles from the nearest park. (Score: High)
Population Density: It's in the second-most densely populated census tract. (Score: High)
Transit: There's a bus stop directly across the street. (Score: High)
Zoning: Zoned for commercial, but a recent zoning change allows for public use with a variance. It's a brownfield site and would require extensive environmental cleanup. (Score: Low)
Initial Conclusion: Good location for community, but significant zoning and environmental hurdles.
Parcel 2: Old Rail Yard
Location: Western edge of Sunnyside.
Analysis:
Green Space Gap: It's a green space gap, but just barely at 0.6 miles from a park. (Score: Medium)
Population Density: It's in a moderately populated area. (Score: Medium)
Transit: It's a 10-minute walk from a bus stop. (Score: Low)
Zoning: It's a large, publicly owned parcel zoned for industrial use. (Score: Medium)
Initial Conclusion: A possible option, but not ideal in terms of community access.
Parcel 3: Pine Street Field
Location: Eastern side of Sunnyside.
Analysis:
Green Space Gap: This area is a major green space gap, with the nearest park 1.2 miles away. (Score: Very High)
Population Density: It is in the most densely populated census tract in Sunnyside. (Score: Very High)
Transit: The Pine Street subway station is just two blocks away. (Score: Very High)
Zoning: The property is owned by the city and is currently a vacant lot zoned for public recreation. No environmental issues are known. (Score: Very High)
Initial Conclusion: This location excels across all key metrics.
Step 4: Self-Consistency Check (Run the analysis again)
I will now re-run this entire analysis with slightly different weightings. For example, what if I prioritize population density over all other factors? What if I prioritize environmental safety above all else?
Run 2 (Prioritizing Population): Parcel 3 and Parcel 1 are the top contenders, with Parcel 3 still slightly ahead due to the more extreme green space gap.
Run 3 (Prioritizing Zoning/Feasibility): Parcel 3 is the clear winner as it requires the least amount of legal and environmental work. Parcel 1 is a non-starter in this run.
Step 5: Final Recommendation
Across all my different reasoning paths and weighting schemes, Parcel 3: Pine Street Field consistently emerges as the optimal choice.
It provides the highest community benefit by addressing a major green space gap in the most densely populated and transit-accessible part of the neighborhood, and it is the most feasible option from a zoning and environmental standpoint.
Final Answer: "Based on my analysis, the optimal location for a new park in the Sunnyside neighborhood is the vacant lot at Pine Street Field.
This location best serves the community by addressing a significant green space gap, is highly accessible by public transit, and is located in the most densely populated area of the neighborhood.
Furthermore, the property is already zoned for recreation and is owned by the city, making it a highly feasible option."