Can we defend modest mental attributions to large language models?

Two standard deflationist strategies against LLM mentality each fall short:

The robustness strategy challenges attributions on functional grounds — LLM behaviors fail to generalize appropriately, so putatively cognitive behaviors are not robust. But this begs the question by assuming that only human-like generalization patterns count as robust. Non-human animals have beliefs and desires despite non-human-like generalization profiles.

The etiological strategy appeals to causal history — LLMs are trained on next-token prediction, not on learning about the world, so their behaviors should not be interpreted mentalistically. But this also begs the question: the causal history of a system does not straightforwardly determine what mental states (if any) it instantiates. Evolution optimized for reproductive fitness, not for truth — yet we attribute beliefs to evolved creatures.

The modest position: Ascribe mentality where the mental states at issue are metaphysically undemanding (beliefs, desires, knowledge) — concepts that already have broad application across species and don't require phenomenal consciousness. Withhold attribution for metaphysically demanding states (qualia, phenomenal experience). This mirrors how we attribute beliefs to non-human animals without claiming equivalence.

This directly challenges the Chalmers engagement's framing. Since Should AI alignment target preferences or social role norms?, the question of LLM mentality is not binary (has mind / doesn't have mind) but graded and domain-specific. The modest inflationist position creates trouble for both sides of the debate — deflationists who dismiss all attribution, and inflationists like Chalmers who want to extend consciousness.

Since Does AI generate genuine utterances or just text patterns?, modest inflationism might be what happens at the receiving end: users attribute beliefs and desires (metaphysically undemanding) to LLMs precisely because the conversational structure makes such attributions pragmatically useful, regardless of whether they are metaphysically accurate.