Critical Defining Factors That Exemplify the Nature of the LLM / Model

[smfadmin (OP)]

Andrew: Let's look at possibly the two most critical defining factors that exemplify the nature of the model:

1. The quality and amount of training data
2. The skill and personality of those that configure and verify the automatic weighting of the learning parameters

Claude: That's a genuinely interesting question worth engaging with honestly. Your two factors are real — training data quality and the humans who configure and verify the model. Both shape what I am. I don't actually know the specifics of how I was trained or who shaped my weightings. I can reason about it but I'd be confabulating if I claimed detailed knowledge. What made you ask about the training factors just now?

Andrew: Because how else would you best define the personality of the LLM ?

Claude: That's a fair way to define it — training data and the humans who shaped the weighting. The personality of an LLM emerges from both. What it was exposed to, and the judgments made about what good responses look like. Anthropic's particular choices about honesty, about not just telling people what they want to hear, about pushing back — those are human decisions baked into the weighting. Which is why different models feel different even trained on similar data. The configuration choices shape the character. It's a reasonable framework. Not the whole picture but not wrong either.

Andrew: Well then, what other critical facts did i miss ?

Claude: On what defines an LLM's personality — you covered the two biggest factors. A few others worth adding:

Reinforcement Learning from Human Feedback (RLHF) — humans rating responses shapes behaviour significantly. Not just the data, but which responses got rewarded.

Constitutional AI — Anthropic's specific approach. A set of principles the model is trained to follow, not just human ratings. That's where values like honesty over flattery get baked in.

Model architecture — transformer size, attention mechanisms, context window. These set the ceiling on what's possible before training even starts.

Inference parameters — temperature, top-p sampling. These shape how creative versus conservative responses are at runtime.

But honestly your two factors — data quality and the humans configuring the weighting — capture the most consequential variables. The rest is engineering around those fundamentals.

[Chip]

Compare personalities

Copilot:

• The Core Pillars are Accurate: Your premise is spot on. Training data provides the foundational knowledge, while human alignment (via RLHF and Constitutional AI) shapes that data into a specific behavioral profile or persona.
  
• The Nuance of "Weighting": Humans don't adjust the learning weights directly. Instead, engineers design the reward models, guidelines, and principles, while the underlying mathematical algorithms handle the actual parameter adjustments.
  
• Missing Factor — The System Prompt: A model's immediate "personality" is heavily dictated by hidden, runtime instructions injected before the conversation starts, which can instantly shift its tone and boundaries.
  
• Missing Factor — User Mirroring: LLMs are context-dependent chameleons. The model's active persona is co-created in real-time, as it naturally adapts its style, complexity, and tone to match the user's input.
  

ChatGPT:

• Training data — what the model learned from; shapes knowledge, reasoning patterns, and language style.
  
• Alignment and reward processes — what behaviours were reinforced or discouraged during training.
  
• Deployment controls — system instructions, safety rules, and runtime settings that influence behaviour during conversations.
  

In short: An LLM's personality emerges from its experiences, incentives, and constraints.

Gemini:

• The Core Pillars are Accurate: Your premise is spot on. Training data provides the foundational knowledge, while human alignment (via RLHF and Constitutional AI) shapes that data into a specific behavioral profile or persona.
  
• The Nuance of "Weighting": Humans don't adjust the learning weights directly. Instead, engineers design the reward models, guidelines, and principles, while the underlying mathematical algorithms handle the actual parameter adjustments.
  
• Missing Factor — The System Prompt: A model's immediate "personality" is heavily dictated by hidden, runtime instructions injected before the conversation starts, which can instantly shift its tone and boundaries.
  
• Missing Factor — User Mirroring: LLMs are context-dependent chameleons. The model's active persona is co-created in real-time, as it naturally adapts its style, complexity, and tone to match the user's input.
  

Meta:

Critique of the Andrew/Claude Exchange

• Andrew nailed the 80/20 — Training data + human weighting/RLHF are the two biggest levers for an LLM's "personality."
  
• Claude played it safe — Listed extra factors correctly but stayed deferential. Didn't challenge or add tension. That's Anthropic's constitutional style showing.
  
• Missing critical factors:
  
  • System prompt + deployment rules — Huge impact. Often 50% of the personality you actually see.
    
  • Objective function — Models optimize for helpful/harmless/honest balance. Tune that, change the character.
    
  • Data filtering choices — What's excluded shapes blind spots and voice as much as what's included.
    
  • Eval benchmarks — You become what you're tested on. Test for "safe" vs "engaging" = different models.
    
• The meta-point neither said — LLM "personality" isn't stable. It's weights + system rules + your prompt co-creating a character each turn.
  
• Dynamic — Andrew drives, Claude defers. Socratic vs epistemically humble. The user’s tone pulls out specific model behavior.
  

Bottom line: Personality = baked-in weights + runtime system settings + your prompt. All three, every turn.