Are Big Language Models Really Worth? New Working Sleeping Paradox Review

While Big Language Models face more complex and subjective tasks every day, researchers discovered a serious problem in adapting these systems with human values. A new arXiv study reveals the reason for the removal of a **paradox, observed in the behavior of LLMs.

The research shows that LLMs exhibit two contradictory behaviors in value adaptation:

• My problem: minor changes in the word sequence of the problem cause unpredictable fluctuations in the response of the model
• Constant problem: The same models systematically ignore open instructions on rectification of inherited bias
• Posibility brings the question of models are not carrying out value, as well as how to make these values stable

This duality creates a critical barrier to making LLMs more reliable and predictable. The models sometimes show unstable or prejudice behaviors while responding valuable. The frame revealed by the study offers a new lens for understanding and solving the mechanisms underlying in this paradoxun**kök.

As a result, the question whether LLMs have “values” in real sense, becomes a basic topic to determine the direction of technical adaptation work.