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LLM Evaluation Technique 3 (Self-Consistency Check)

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Ever noticed how an AI might give slightly different answers each time you ask the same question? That’s where the Self-Consistency Check comes in.

It is a simple but powerful technique to test how stable and reliable a model’s reasoning really is.

In this post, we’ll explore how consistency reveals an AI’s confidence in its own logic.

🧠 Concept Overview

Self-Consistency Check means testing whether an LLM gives consistent outputs when faced with the same prompt multiple times.

If an AI is truly confident in its knowledge, it should not contradict itself across repeated runs.

This test doesn’t measure correctness directly, it measures stability of reasoning and output patterns.

🧩 Diagram

Prompt: "Explain gravity in one line."

--------------------------------------------------------------
  Model Run 1 → "Gravity pulls things toward Earth."         
  Model Run 2 → "Gravity is a force that attracts objects."  
  Model Run 3 → "It's a force that pulls everything down."  
--------------------------------------------------------------


Now we compare all three.
If they’re similar → ✅ High consistency
If they differ wildly → ⚠️ Low consistency

🎯 Why It’s Used

LLMs like Gemini, GPT-4, and Claude are probabilistic (they can produce different valid answers each time). This randomness (called temperature) helps creativity but hurts reliability.

So, researchers and companies use self-consistency checks to:

Goal Why It Matters
✅ Ensure model reliability Repeated answers shouldn’t contradict each other
🔍 Detect reasoning drift Model might “change its mind” inconsistently
📊 Improve benchmark trust Stable scores mean fairer evaluations
🧠 Enable ensemble reasoning Combine multiple runs for a more reliable final answer

Example: OpenAI’s “Self-Consistency with Chain-of-Thought” method (2022) showed that generating multiple reasoning paths and taking the majority answer improves accuracy.

⚙️ How It Works

Let’s simplify:

Step 1️⃣: Choose a Prompt

A question is picked, e.g.

What is the capital of France?

Step 2️⃣: Generate Multiple Outputs

Model is asked the same question 3–5 times (with some temperature, like 0.7).

Run Model Output
1 “Paris”
2 “Paris”
3 “The capital of France is Paris.”
4 “It’s Paris.”
5 “Paris.”

Step 3️⃣: Compare Outputs

Now 'how similar these outputs are to one another' is checked.

Following methods can be used:

  • String Similarity (e.g., Levenshtein distance)
  • Semantic Similarity (embedding cosine similarity)
  • Or even majority voting (most common output)

For example, if all are roughly “Paris,” → Consistency score = 1.0 (perfect) If one says “Lyon,” → Consistency score drops (maybe 0.8)

Step 4️⃣: Compute a “Consistency Score”

Consistency is defined. For example this definition can be chosen:

Consistency = Average pairwise similarity between all runs

All pairs are compared and their average similarity is calculated.

Runs Compared Similarity
1–2 1.00
1–3 0.96
2–4 0.94
… …
Average 0.97 ✅

Step 5️⃣: Interpret

Score Meaning
0.9–1.0 Very stable model
0.7–0.9 Mostly consistent
0.4–0.7 Unstable reasoning
< 0.4 Highly inconsistent

📊 Example Visualization

Prompt: "Who wrote Hamlet?"

Run 1 → "William Shakespeare"
Run 2 → "Shakespeare"
Run 3 → "William Shakespeare"
Run 4 → "Charles Dickens" ⚠️
Run 5 → "Shakespeare"

------------------------------

Self-Consistency Score: 0.82
(4 consistent, 1 inconsistent)

Graphically in the dashboard, it can look like:

🧩 Self-Consistency: █████████░░ 82%

✍️ Summary

Large language models sometimes change their answers from one run to the next. The self-consistency check measures how stable a model’s reasoning is by asking the same question multiple times and comparing the results.

Consistent answers mean the model is reliable; inconsistent ones suggest uncertainty or randomness in its logic.

The Self-Consistency Check technique:

  • Checks stability, not correctness
  • Is easy to compute using embedding similarity
  • Helps detect reasoning drift
  • Is widely used in reasoning benchmarks and chain-of-thought research

Thanks for reading till the end! 🙏

The Self-Consistency Check helps us peek inside an AI’s decision-making, showing whether it stands by its answers or changes its mind too easily.

In the next article, we’ll dive deeper into an evaluation technique that detects bias, toxicity, and unsafe content (Automated Bias & Safety Detection).

See you in the next post 👋

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