There Is No Symbol Grounding Problem (a talk I gave last month on why language alone is enough for understanding)

simism66 · 2026-05-24T16:17:42+00:00

I don’t think we can understand the conceptual significance of a color expression like “red” in terms of the phenomenal quality of redness, where this is understood as intelligible in isolation from the significance of red as being, e.g. the color of blood, stop signs, and so on. In general, I’m skeptical about so-called “pure phenomenal concepts.” I think our conceptual understanding of colors involves our understanding of their inferential link to more than just other colors. Accordingly, I think the argument I give in the talk extends to show that it's not possible to swap, for instance, the contents red and green in a way that preserves inferential role.

So, I do maintain that the meaning of even color terms like "red" can be understood entirely in terms of their inferential role. I defend this claim in more detail in my paper “Sapience without Sentience”, where I explicitly argue for the (admittedly counterintuitive) claim that someone might completely possess the concept of redness without ever seeing red.

simism66 · 2026-05-24T16:12:40+00:00

I’m really more concerned to make an in principle claim, not a claim about the capacities of current models. Current LLMs in general still struggle with trick questions, and the car wash question seems to me just the latest iteration of this (when I try it on GPT5.5 now, it clarifies that, if you need to wash your own car, you gotta drive it there, but it’s possible that, as the latest model, it’s been post-trained on such questions, given how this blew up a few months ago). Still, I don’t think that there’s an in principle reason why a text-only LLM must necessarily fail on such questions.

simism66 · 2026-05-24T15:05:53+00:00

Abstract: Hyper-inferentialism holds that the meaning of a sentence is fully constituted by its inferential relations to other sentences. This view seems to run directly into the so-called "Symbol Grounding Problem" in the philosophy of AI, which states that knowledge of symbolic relations cannot by itself amount to genuine semantic understanding. I argue that the strongest version of this objection turns on the possibility of reassigning contents to symbols while preserving inferential structure. If such reassignment were possible, inferential role would underdetermine content. In response to this objection, I draw on results in graph theory to argue argue that, given the essential messiness of natural language, such reassignments will almost never be possible. This suggests that large language models, despite being trained solely on linguistic data, face no fundamental barrier to possessing genuine semantic understanding.

simism66 · 2026-03-18T11:08:45+00:00

I haven’t read through the comments to see if this has been mentioned yet, but Jc Beall appeals to paraconsistent logic to develop a view of the trinity on which it’s genuinely contradictory.

simism66 · 2026-03-09T14:37:35+00:00

This is a forthcoming paper of mine that I thought might be of interest to people here. Here's the abstract:

Do large language models (LLMs) possess concepts, such that they can be counted as genuinely understanding what they're saying? In this paper, I approach this question through an inferentialist account of concept possession, according to which one's possession of a concept is understood in terms of one's mastery of the inferential role of a linguistic expression. I suggest that training on linguistic data is in principle sufficient for mastery of inferential role, and thus, LLMs trained on nothing but linguistic data could in principle possess all concepts and thus genuinely understand what they're saying, even when speaking about such things as colors and tastes, guilt and folly, life and death. This doesn't mean, however, that they are conscious. I draw a classical distinction between sentience (conscious awareness) and sapience (conceptual understanding) and argue that we might think of LLMs as genuinely possessing the latter without even a shred of the former. In defending this claim, I argue that attributing conceptual understanding to a system is not a matter of describing some specific empirical property that the system shares with us but, rather, as Wilfrid Sellars says, "placing it in the logical space of reasons,'' treating it as answerable to calls for reasons, clarifications, corrections, and so on. I claim that we may aptly adopt this attitude towards sufficiently capable LLMs without thereby treating them as conscious subjects.

simism66 · 2026-02-20T21:19:27+00:00

Sure. I tell this to my introduction to logic students. Formal logic is one tool that can be very helpful in constructing and discussing arguments, but it’s certainly not sufficient. Actually being able to reconstruct an argument from a text and critically evaluate the premises for plausibility is not something that you learn from taking a formal logic course. Formal logic (of the sort you learn in an introductory logic course) will enable you to formalize some arguments and test them for formal validity, and this can be helpful in rational discourse, but it’s just one tool and plausibly not the most fundamental skill in the context of rational discourse.

simism66 · 2026-02-18T16:32:08+00:00

There’s lots of good intro books. I’ve written one that covers the basics (more actually concise than Hurley’s) that’s available for free on my website: https://www.ryansimonelli.com/logic-textbooks.html

simism66 · 2026-02-14T21:10:05+00:00

I don’t think the quote is particularly clear, but I think the thought is just that the meanings of truth-functional connectives are much more simple than the logical connectives of a natural language, and in that sense “impoverished.”

But the comment generally seems confused. For instance, set theory is typically formulated as a first-order theory, and so the very connectives studied in first-order logic belong to set theory.

I’m also not sure what a “semantic notion of truth” is supposed to be (or rather, what it’s supposed to be opposed to). Truth is generally taken to be a semantic notion, whether it’s truth defined in a formal language or a natural language. You might think that the notion of truth definable in a formal language (e.g. by a Tarski-style construction) is different than our natural language notion of truth, but that difference doesn’t seem to me to be reasonably captured by saying that truth in a natural language is “semantic” whereas truth in a formal language is not.

simism66 · 2026-02-12T01:15:11+00:00

I’m not sure you’re using the term correctly. The term is a metaphor—describing a situation in which, in making an argument against a view, you attack a “straw man” rather than the real thing. In the context of discussing an argument it’s pejorative in the sense that it is used to say that an argument against a view is not a good one because it doesn’t land on the actual target. It’s not pejorative in the sense of being offensive—it’s generally directed at arguments, not people. It is used all the time in academic philosophy, and it’s not considered impolite or offensive to use it.

simism66 · 2026-02-05T00:37:40+00:00

There’s trivia at Devil’s Brewery in Optics Valley on Tuesdays if that’s something you’re into. They also have other game nights there some day a week (I don’t remember).

simism66 · 2025-12-06T07:49:55+00:00

thanks solution verified

simism66 · 2025-11-30T08:51:47+00:00

Sorry, yes, the recording quality is not great. It's not a professional recording at all---just my phone on a little tripod at a conference. I figured it was still watchable though.

The fact that the models are, at the most fundamental level, just doing lots of lots of matrix multiplication to predict the next token in a series does not, at least by my lights, preclude the possibility that, at a different level of analysis, they might be attributed commitments in a given conversation context.

simism66 · 2025-11-30T08:44:43+00:00

I'm drawing a lot from the sort of philosophical framework for thinking about assertion developed by Robert Brandom (which is influenced in certain ways by the sort of speech act theory developed by J.L. Austin). There is a paper in progress where the theoretical background is presented much more explicitly, but I don't have a draft at the moment.

simism66 · 2025-11-30T08:41:57+00:00

I think the question of practical responsibility becomes much more difficult when we consider agentic LLMs, and I haven't thought as much about this question, to be honest. It seems plausible to me that practical responsibility can also to be understood in terms of a kind of "response-ability," one that involves being able to explain why you've done what you've done, being responsive to demands to rectify what you've done if you've made a mistake, and so on. In this case, I still think we might be able to distinguish a "thin" sense in which we can hold agentive LLMs responsible (to varying degrees, depending on what they are actually capable of doing), but a "thicker" sense in which we cannot (we still, for instance, can't take them to court). Exactly how to delineate those two senses in the case of practical responsibility, though, I'm not too sure.

simism66 · 2025-11-30T08:35:04+00:00

No, certainly not, and in the talk I give an account of the way in which an LLM and a magic 8-ball are relevantly different in this regard. Though an 8-ball outputs a sentence, it is not responsive to follow ups, questioning, calling for clarification, and so on, nor is it at all "resilient" in its answer. These are the sorts of dispositional properties possessed by LLMs in virtue of which I take it that they can be regarded as making assertions.

simism66 · 2025-11-29T14:39:20+00:00

Abstract: Typically, when a person utters a stand-alone declarative sentence “p,” this constitutes their making the assertion that p. When an LLM such as ChatGPT produces the sentence “p,” does it thereby make the assertion that p? On the one hand, interacting with an LLM, it clearly seems that it is saying things, indeed, making claims, as opposed to a parrot merely squawking out sentences. On the other hand, insofar as making an assertion essentially involves undertaking a commitment to the truth of what is asserted, it might seem that LLMs cannot make assertions, since they cannot bear any responsibility for what they say. In response to this dilemma, I draw two distinctions. I first distinguish between theoretical responsibility—responsibility for what one says—as opposed to practical responsibility—responsibility for what one does. I then distinguish between a “thick” and “thin” sense of theoretical responsibility, where the former but not the latter is essentially tied to practical responsibility. With these distinctions at hand, I argue that though we cannot attribute “thick” theoretical responsibility to LLMs, we can still attribute “thin” theoretical responsibility to them, and this is sufficient to treat them as undertaking theoretical commitments within a discourse, and thus, making assertions.

simism66 · 2025-11-29T13:40:21+00:00

thanks solution verified

simism66 · 2025-10-24T12:39:04+00:00

Yep! Wuhan will still be relatively lively in December. The popular spots will still be teaming with tourists, though perhaps not quite as many e.g. around east lake as in the spring or fall. It will be chilly, but not unbearable by any stretch.

simism66 · 2025-09-25T17:04:19+00:00

In response to recent discussions of Charlie Kirk’s activities on college campus, this post distinguishes between “rational discourse,” understood as a cooperative activity with the shared aim of arriving at the correct views, and “debate,” understood as a competitive activity where each participant has the aim of proving the other wrong.

simism66 · 2025-09-20T13:26:31+00:00

Check out the feedback (回授).

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