Why LLMs Write Bland Text: 6 Technical Causes and the Path to Fixing It
scaling01 · x · 2026-08-10
The author explores why current Large Language Models (LLMs) often produce bland, non-committal text, identifying six technical factors:
- Objective Function Limits: Next-token prediction inherently tries to preserve optionality, leading to ambiguous outputs.
- Alignment Side Effects: Hallucination training makes models overly cautious and wary of expressing strong opinions.
- Lack of Intent Labels: Training data lacks labels for what the text intends to convey, making generated text feel aimless.
- Data Imbalance: Training data likely contains far more curated blogs and articles than everyday human talk, making conversations feel too polished.
- Rigid Rubrics: Writing reinforcement learning (RL) probably relies on general rubrics judging quality and coherence, which suppresses uniqueness.
- Assistant Persona Drawbacks: The general agreeable assistant persona is the opposite of what makes humans interesting—being odd and offering new perspectives.
The author notes that human writing feels more authentic because it is direct and decisive without trying to be fancy. These issues are 100% fixable today and will likely be resolved with continual learning.
Related event: Why LLM-Generated Text Often Feels Bland(2 posts)→
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