From the source
There’s a common misconception in tech that humans are subjective, so we should rely on AI for objective evaluation.
But the reality is that LLMs are also subjective.
Just like human reviewers, they can carry biases: Verbosity bias : LLM judges usually favor longer, confident-sounding answers , even when the underlying facts are wrong.
Position bias : Swapping the order of two options can cause an LLM judge to flip its verdict over 75% of the time .
The halo effect : Because LLMs generate text piece by piece, clean grammar and formatting can trick an AI judge into giving top marks for accuracy while missing errors.
Agreeableness bias : LLM judges are great at recognizing valid work.
But with detecting errors, they may fail to catch incorrect, buggy, or unsafe outputs up to 75% of the time .
Self-enhancement bias: Also known as egocentric bias, AI evaluators systematically overrate outputs that were generated by themselves or from their own family.
Ultimately, LLM judges are not objective arbiters of truth.
They are subjective models with their own distinct biases.
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