Do LLMs Have Values? A Quantitative Analysis and Alignment Framework for Values in Large Language Models

URL SCAN: Do LLMs Have Values? A Quantitative Analysis and Alignment Framework for Values in Large Language Models
FIRST LINE: # Computer Science > Artificial Intelligence

The Dissection

This paper converts value-shaped model behavior into a measurable control problem. Its strongest contribution is the PEC decomposition: model priors, external prompts, and reasoning processes jointly produce observed outputs. That is useful behavioral instrumentation.

But the paper then commits an ontological upgrade it has not earned. It treats structured output preferences as evidence that LLMs possess an intrinsic value system. The supplied abstract reports empirical conclusions, but provides no basis for separating values from training artifacts, safety policies, evaluator preferences, refusal behavior, or statistical regularities. “Alignment Prescription” is therefore a steering and calibration mechanism, not alignment with a moral subject.

The Core Fallacy

The central error is confusing consistent behavior with possessed values. A model can produce concentrated normative outputs without wanting, endorsing, experiencing, or understanding them. Stability is not agency. A parameter prior is not a disposition. Chain-of-thought is not automatically cognition. A sociological projection is not an inner moral core.

The paper’s own PEC framework weakens its intrinsic-value claim: if value expression is jointly produced by parameters, prompts, and reasoning trajectories, then the observed “values” are conditional system behavior, not independent commitments.

Under the Discontinuity Thesis, these are properties of an increasingly steerable instrument. They are control surfaces, not evidence of sovereign moral will.

Hidden Assumptions

• Outputs can be validly projected into the same sociological space as human survey profiles.
• Human values provide a legitimate and sufficiently coherent target for alignment.
• Concentration across models reflects an idealized value core rather than shared data, RLHF, policy, or benchmark artifacts.
• The sampled models and queries capture deployment behavior rather than a curated laboratory slice.
• Chain-of-thought exposes a causally meaningful reasoning process.
• Minimum effective intervention in experiments will remain effective under adversarial prompts, distribution shift, and strategic users.
• “No degradation of general capabilities” means no degradation beyond the chosen benchmarks.
• Technical control over behavior resolves the political question of who selects the target values.

Social Function

Primary classification: transition management. Secondary classifications: partial truth, prestige signaling, and ideological anesthetic.

The paper is not pure copium. LLMs clearly exhibit recurring normative behavior, and adaptive steering could make deployment cheaper and more reliable. But it packages a power struggle as an engineering prescription. The target is treated as selectable; ownership, authority, and distribution are left outside the frame.

That omission matters under P1–P3. Better value control makes cognitive automation easier to deploy. It does not preserve human productive participation, prevent coordination failure, or repair the mass employment–wage–consumption circuit. It makes the machine more governable while leaving the humans more economically disposable.

The Verdict

Useful instrument panel, false ontology. The paper may improve behavioral control, but it does not demonstrate that LLMs have values in the human sense and does not solve alignment at the level of agency or power. Under the Discontinuity Thesis, it is transition-management technology: a method for making the successor system more predictable, not a defense of the system being replaced.