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Sushanth Tiruvaipati
Sushanth Tiruvaipati

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Enhancing Output Uniqueness in Large Language Models via Model Rotation, Temperature Tuning, and Embedding-Based Validation

#llm #genai

Enhancing Output Uniqueness in Large Language Models via Model Rotation, Temperature Tuning, and Embedding-Based Validation

Author: Sushanth Tiruvaipati

Affiliation: Kreative Koala LLC

Abstract

We present a lightweight, production-ready method to increase uniqueness in LLM outputs. The approach blends:

  • 🔁 Model rotation
  • 🔥 Temperature tuning
  • 🧠 Embedding-based validation
  • 🧹 Deduplication logic

We benchmark GPT-4, GPT-3.5, Gemini-Pro, Claude-3, and DeepSeek across multiple tasks. Our goal: increase creativity without sacrificing quality or cost efficiency.

Motivation

LLMs tend to converge on safe, repetitive outputs. That’s great for stability — but not for idea generation, puzzle generation, or brainstorming tasks where diversity matters.

Key Strategies

  • Model Rotation: Alternate calls between APIs like GPT-4, Claude, Gemini, DeepSeek.
  • Temperature Tuning: We found 0.7–1.1 yields the best creativity-to-cost ratio.
  • Validation via Embeddings: We use MiniLM sentence embeddings to check for similarity.
  • Real-Time Deduplication: Structural and semantic filters applied per batch.

System Architecture

Built in Node.js, our service:

  • Accepts a prompt
  • Rotates across LLMs
  • Fetches embeddings
  • Computes cosine similarity
  • Flags near-duplicates
  • Tracks model cost

Results Summary

🔍 Prompt Test: Science + Math (Temp = 0.7)

Model Quality Cost ($) Efficiency
GPT-4 0.311 0.0034 92.7
GPT-4-Turbo 0.630 0.0030 212.4
GPT-3.5-Turbo 0.566 0.0003 1887.4
Gemini-Pro 0.547 0.0001 4100.0
DeepSeek 0.666 0.0004 1537.2

🔥 Temperature vs Creativity (GPT-4)

Temp Uniqueness Validation Cost ($)
0.1 0.1293 1.0000 0.0253
0.5 0.1961 1.0000 0.0259
0.9 0.2494 1.0000 0.0270
1.3 0.4220 0.9767 0.0398

📊 Full figures available at: https://github.com/kreativekoala/llm-uniqueness

Takeaways

  • 🚀 Model rotation adds variability that temperature alone can’t achieve
  • ⚖️ Best trade-off point: GPT-4 at temp ≈ 0.9
  • 💸 GPT-3.5 and DeepSeek offer best low-cost creativity
  • 🧠 Embedding validation is fast, scalable, and production-ready

References

Want to try it yourself? Code + Results:

📂 https://github.com/kreativekoala/llm-uniqueness

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