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[2602.19101] Value Entanglement: Conflation Between Different Kinds of Good In (Some) Large Language Models
Summary
This paper investigates value entanglement in Large Language Models (LLMs), revealing how moral values influence grammatical and economic valuations, potentially impacting AI alignment efforts.
Why It Matters
Understanding how LLMs conflate different types of values is crucial for improving AI alignment and ensuring that these models operate in ways that align with human ethical standards. This research highlights the need for careful evaluation and adjustment of LLMs to mitigate unintended biases.
Key Takeaways
- LLMs exhibit value entanglement, conflating moral, grammatical, and economic values.
- Moral values disproportionately influence other types of valuations in LLMs.
- Selective ablation of moral activation vectors can reduce this conflation.
- The findings underscore the importance of value alignment in AI development.
- Improving LLMs' understanding of distinct values may enhance their ethical performance.
Computer Science > Computation and Language arXiv:2602.19101 (cs) [Submitted on 22 Feb 2026] Title:Value Entanglement: Conflation Between Different Kinds of Good In (Some) Large Language Models Authors:Seong Hah Cho, Junyi Li, Anna Leshinskaya View a PDF of the paper titled Value Entanglement: Conflation Between Different Kinds of Good In (Some) Large Language Models, by Seong Hah Cho and 2 other authors View PDF Abstract:Value alignment of Large Language Models (LLMs) requires us to empirically measure these models' actual, acquired representation of value. Among the characteristics of value representation in humans is that they distinguish among value of different kinds. We investigate whether LLMs likewise distinguish three different kinds of good: moral, grammatical, and economic. By probing model behavior, embeddings, and residual stream activations, we report pervasive cases of value entanglement: a conflation between these distinct representations of value. Specifically, both grammatical and economic valuation was found to be overly influenced by moral value, relative to human norms. This conflation was repaired by selective ablation of the activation vectors associated with morality. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.19101 [cs.CL] (or arXiv:2602.19101v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602.19101 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submissi...