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505 670-9918 Santa Fe, NM On Mon, Sep 15, 2025, 9:18 AM glen <[email protected]> wrote: > VaultGemma: A Differentially Private Gemma Model > https://services.google.com/fh/files/blogs/vaultgemma_tech_report.pdf > > While this is great for privacy, I think the fuzziness it provides applies > to all facts, right? So if someone's out there defaming π, some of that > defamation may well bleed over into what we do or do not know about e. I > guess it depends on the number of doclets defaming π and how the defamation > is constructed. So when π and e had their viral courtroom battle of the > abuse in their relationship, if the majority of doclets assert that e is an > attention-seeking gold-digger, that same criticism might apply to π or > worse, all the irrational numbers? Regular Joes like √2 will suffer the > most from the celebrity fuzz. > > So does DP-SGD fuzzify all facts? What's the impact on fidelity/accuracy? > Maybe it'll make the output more trustworthy because it'll subtly alter > statements to make them more general, less specific, less concrete? But > then if there's a domain where there's one valiant soul shouting truth in a > wilderness of bullsh¡t, it'll be less likely to make it into the model? > > > On 9/9/25 5:19 PM, glen wrote: > > It's unfortunate jargon [⛧]. So it's nothing like whether an LLM is red > (unless you adopt a jargonal definition of "red"). And your example is a > great one for understanding how language fluency *is* at least somewhat > correlated with fidelity. The statistical probability of the phrase "LLMs > hallucinate" is >> 0, whereas the prob for the phrase "LLMs are red" is > vanishingly small. It would be the same for black swans and Lewis Carroll > writings *if* they weren't canonical teaching devices. It can't be that > sophisticated if children think it's funny. > > > > But imagine all the woo out there where words like "entropy" or > "entanglement" are used falsely. IDK for sure, but my guess is the false > sentences outnumber the true ones by a lot. So the LLM has a high > probability of forming false sentences. > > > > Of course, in that sense, if a physicist finds themselves talking to an > expert in the "Law of Attraction" (e.g. the movie "The Secret") and makes > scientifically true statements about entanglement, the guru may well judge > them as false. So there's "true in context" (validity) and "ontologically > true" (soundness). A sentence can be true in context but false in the world > and vice versa, depending on who's in control of the reinforcement. > > > > > > [⛧] We could discuss the strength of the analogy between human > hallucination and LLM "hallucination", especially in the context of > prediction coding. But we don't need to. Just consider it jargon and move > on. > > > > On 9/9/25 4:37 PM, Russ Abbott wrote: > >> Marcus, Glen, > >> > >> Your responses are much too sophisticated for me. Now that I'm retired > (and, in truth, probably before as well), I tend to think in much simpler > terms. > >> > >> My basic point was to express my surprise at realizing that it makes as > much sense to ask whether an LLM hallucinates as it does to ask whether an > LLM is red. It's a category mismatch--at least I now think so. > >> _ > >> _ > >> __-- Russ <https://russabbott.substack.com/> > >> > >> > >> > >> > >> On Tue, Sep 9, 2025 at 3:45 PM glen <[email protected] <mailto: > [email protected]>> wrote: > >> > >> The question of whether fluency is (well) correlated to accuracy > seems to assume something like mentalizing, the idea that there's a > correspondence between minds mediated by a correspondence between the > structure of the world and the structure of our minds/language. We've > talked about the "interface theory of perception", where Hoffman (I think?) > argues we're more likely to learn *false* things than we are true things. > And we've argued about realism, pragmatism, prediction coding, and > everything else under the sun on this list. > >> > >> So it doesn't surprise me if most people assume there will be more > true statements in the corpus than false statements, at least in domains > where there exists a common sense, where the laity *can* perceive the > truth. In things like quantum mechanics or whatever, then all bets are off > becuase there are probably more false sentences than true ones. > >> > >> If there are more true than false sentences in the corpus, then > reinforcement methods like Marcus' only bear a small burden (in lay > domains). The implicit fidelity does the lion's share. But in those domains > where counter-intuitive facts dominate, the reinforcement does the most > work. > >> > >> > >> On 9/9/25 3:12 PM, Marcus Daniels wrote: > >> > Three ways some to mind.. I would guess that OpenAI, Google, > Anthropic, and xAI are far more sophisticated.. > >> > > >> > 1. Add a softmax penalty to the loss that tracks non-factual > statements or grammatical constraints. Cross entropy may not understand > that some parts of content are more important than others. > >> > 2. Change how the beam search works during inference to skip > sequences that fail certain predicates – like a lookahead that says “Oh, I > can’t say that..” > >> > 3. Grade the output, either using human or non-LLM supervision, > and re-train. > >> > > >> > *From:*Friam <[email protected] <mailto: > [email protected]>> *On Behalf Of *Russ Abbott > >> > *Sent:* Tuesday, September 9, 2025 3:03 PM > >> > *To:* The Friday Morning Applied Complexity Coffee Group < > [email protected] <mailto:[email protected]>> > >> > *Subject:* [FRIAM] Hallucinations > >> > > >> > OpenAI just published a paper on hallucinations < > https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4aaa5/why-language-models-hallucinate.pdf > < > https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4aaa5/why-language-models-hallucinate.pdf>> > as > well as a post summarizing the paper < > https://openai.com/index/why-language-models-hallucinate/ < > https://openai.com/index/why-language-models-hallucinate/>>. The two of > them seem wrong-headed in such a simple and obvious way that I'm surprised > the issue they discuss is still alive. > >> > > >> > The paper and post point out that LLMs are trained to generate > fluent language--which they do extraordinarily well. The paper and post > also point out that LLMs are not trained to distinguish valid from invalid > statements. Given those facts about LLMs, it's not clear why one should > expect LLMs to be able to distinguish true statements from false > statements--and hence why one should expect to be able to prevent LLMs from > hallucinating. > >> > > >> > In other words, LLMs are built to generate text; they are not > built to understand the texts they generate and certainly not to be able to > determine whether the texts they generate make factually correct or > incorrect statements. > >> > > >> > Please see my post < > https://russabbott.substack.com/p/why-language-models-hallucinate-according > < > https://russabbott.substack.com/p/why-language-models-hallucinate-according>> > elaborating on this. > >> > > >> > Why is this not obvious, and why is OpenAI still talking about > it? > >> > > > -- > ¡sıɹƎ ןıɐH ⊥ ɐןןǝdoɹ ǝ uǝןƃ > Ἐν τῷ ἄλλοις αἴλουροι τὰς ἐχθροὺς ὀξύνονται, ἐγὼ τοὺς φίλους μου ὀξύνομαι > ἵνα σῶμαι αὐτούς. > > > .- .-.. .-.. / ..-. --- --- - . .-. ... / .- .-. . / .-- .-. --- -. --. / > ... --- -- . / .- .-. . / ..- ... . ..-. ..- .-.. > FRIAM Applied Complexity Group listserv > Fridays 9a-12p Friday St. Johns Cafe / Thursdays 9a-12p Zoom > https://bit.ly/virtualfriam > to (un)subscribe http://redfish.com/mailman/listinfo/friam_redfish.com > FRIAM-COMIC http://friam-comic.blogspot.com/ > archives: 5/2017 thru present > https://redfish.com/pipermail/friam_redfish.com/ > 1/2003 thru 6/2021 http://friam.383.s1.nabble.com/ >
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