  As a relative quality comparison of model responses, human and Large Language
Model (LLM) preferences serve as common alignment goals in model fine-tuning
and criteria in evaluation. Yet, these preferences merely reflect broad
tendencies, resulting in less explainable and controllable models with
potential safety risks. In this work, we dissect the preferences of human and
32 different LLMs to understand their quantitative composition, using
annotations from real-world user-model conversations for a fine-grained,
scenario-wise analysis. We find that humans are less sensitive to errors, favor
responses that support their stances, and show clear dislike when models admit
their limits. On the contrary, advanced LLMs like GPT-4-Turbo emphasize
correctness, clarity, and harmlessness more. Additionally, LLMs of similar
sizes tend to exhibit similar preferences, regardless of their training
methods, and fine-tuning for alignment does not significantly alter the
preferences of pretrained-only LLMs. Finally, we show that preference-based
evaluation can be intentionally manipulated. In both training-free and
training-based settings, aligning a model with the preferences of judges boosts
scores, while injecting the least preferred properties lowers them. This
results in notable score shifts: up to 0.59 on MT-Bench (1-10 scale) and 31.94
on AlpacaEval 2.0 (0-100 scale), highlighting the significant impact of this
strategic adaptation. Interactive Demo:
https://huggingface.co/spaces/GAIR/Preference-Dissection-Visualization Dataset:
https://huggingface.co/datasets/GAIR/preference-dissection Code:
https://github.com/GAIR-NLP/Preference-Dissection
