Value Entanglement: Conflation Between Different Kinds of Good In (Some) Large Language Models
arXiv:2602.19101v2 Announce Type: replace-cross 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
This research emerges as the discussion around AI safety and alignment intensifies, highlighting critical challenges in developing truly nuanced and human-like AI reasoning. The paper's publication demonstrates an ongoing academic effort to understand fundamental AI limitations.
A strategic reader should care because 'value entanglement' points to a fundamental limitation in current LLM architectures, suggesting that achieving robust, ethically aligned AI requires more sophisticated models of value representation. This affects AI trustworthiness and deployment in sensitive areas.
This research changes the understanding of how LLMs process ethical and practical distinctions, indicating that their internal representations of 'good' are often conflated, challenging assumptions about their alignment capabilities. It underscores the difficulty of imbuing AI with human-like moral reasoning.
- · AI safety researchers
- · Developers of next-gen AI architectures
- · Ethical AI frameworks and auditors
- · Developers relying on superficial value alignment in LLMs
- · Applications requiring nuanced ethical judgment from AI
Further research and development will be directed towards disentangling value representations in LLMs.
This could lead to a re-evaluation of AI deployment in fields requiring complex moral or ethical decision-making, increasing scrutiny on autonomous systems.
Ultimately, the progress on this problem could bifurcate AI development into systems with 'primitive' vs. 'advanced' value alignment, impacting their societal roles.
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Read at arXiv cs.AI