Data Shapes a Model's Personality
Every large language model has a personality. Some strict, some generous, some picky. That style doesn't appear by chance — it comes from the data. And you can see it in how they judge "accuracy."
This dataset accuracy challenge pushed me to think more creatively about what "accurate" even means and the broader context of opportunity in this space.
The Problem
- A binary rubric (accurate vs inaccurate) flattens nuance.
- A small slip gets treated the same as a major error.
- Clients lose confidence when they see "errors" they don't agree with.
The Experiment
I asked three LLMs (ChatGPT, Claude, Gemini) to re-check the same items and give the re-evaluation.
Key Findings
- • They disagreed on every prompt.
- • One flagged a Beatles song error, another ignored it.
- • One demanded strict scientific detail, others accepted simplifications.
Each model showed a different tolerance for error — almost like a personality.
The Flexible Explainer
More forgiving, willing to accept simplified answers as "good enough." Focused on clarity and usability, even if it glosses over details.
The Strict Teacher
Always checking the rules, quick to call something wrong if it wasn't exact.
The Picky Fact-Checker
Very precise, catching errors others missed (like "Imagine" not being a Beatles song). But it also nitpicked tiny details that didn't really change the meaning.
Strict vs Useful
At first I assumed stricter = better. But it's not that simple.
Minor vs Major: Gemini flagged a minor song title ('I Can't Get No Satisfaction' vs. '(I Can't Get No) Satisfaction') as inaccurate. True, but it missed the point.
When It Matters: On asbestos, strictness mattered. "Synthetic vs natural" changes meaning entirely.
The strictness is only useful when it protects meaning, not when it nitpicks away from usefulness.
The Opportunity for Surge
Surface disagreement as value
Where models diverge is exactly where a team like Surge can shine — to explain, to align, to make nuance visible. These LLM disagreements present higher ROI datasets for Surge, because in simple cases most models converge.
Shape model personality through data
This is the part that excites me most. These disagreements/edge cases aren't errors — they're the life events that shape how a model sees the world. Just as people develop personality through experience, models develop style through data.
By guiding how borderline cases are judged, Surge has the chance to instill qualities like precision, generosity, creativity, or practicality into a model. It's the art of model character design.
Redefine evaluation as a product
Instead of QA as a service, labeling as a service, what if we can make rubrics, reasoning, and disagreement part of the client experience — a self-serve dashboard, alignment on edge cases, and even connecting long-term model traits to concrete datasets.
The Art of Model Personality
It's really fascinating to see the disagreements in this case, whether with a client's QA team or across LLMs, because they show where human judgment matters most.
For simple facts, models converge. For nuanced cases, they diverge — and that's where Surge adds the most value: bridging machine judgment with human expectations, and turning evaluation into the foundation of model personality.
"Data is like the brushstroke that defines the style of a model. Just like great artists have their own strokes — Picasso's abstract lines, Van Gogh's dotted swirls — I feel excited about shaping the data brushstrokes that will create the great art of LLMs."
Let's Discuss This Further
I'd love to hear feedback on this perspective. How do you see the balance between serving immediate client QA needs and shaping longer-term model personality?