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Ask HN: How are Markov chains so different from tiny LLMs?

204 points JPLeRouzic | 2 comments | 17 Nov 25 20:36 UTC | HN request time: 0s | source

I polished a Markov chain generator and trained it on an article by Uri Alon and al (https://pmc.ncbi.nlm.nih.gov/articles/PMC7963340/).

It generates text that seems to me at least on par with tiny LLMs, such as demonstrated by NanoGPT. Here is an example:

  jplr@mypass:~/Documenti/2025/SimpleModels/v3_very_good$
  ./SLM10b_train UriAlon.txt 3
  
  Training model with order 3...
  
  Skip-gram detection: DISABLED (order < 5)
  
  Pruning is disabled
  
  Calculating model size for JSON export...
  
  Will export 29832 model entries
  
  Exporting vocabulary (1727 entries)...
  
  Vocabulary export complete.
  
  Exporting model entries...
  
    Processed 12000 contexts, written 28765 entries (96.4%)...
  
  JSON export complete: 29832 entries written to model.json
  
  Model trained and saved to model.json
  
  Vocabulary size: 1727
  
  jplr@mypass:~/Documenti/2025/SimpleModels/v3_very_good$ ./SLM9_gen model.json
Aging cell model requires comprehensive incidence data. To obtain such a large medical database of the joints are risk factors. Therefore, the theory might be extended to describe the evolution of atherosclerosis and metabolic syndrome. For example, late‐stage type 2 diabetes is associated with collapse of beta‐cell function. This collapse has two parameters: the fraction of the senescent cells are predicted to affect disease threshold . For each individual, one simulates senescent‐cell abundance using the SR model has an approximately exponential incidence curve with a decline at old ages In this section, we simulated a wide range of age‐related incidence curves. The next sections provide examples of classes of diseases, which show improvement upon senolytic treatment tends to qualitatively support such a prediction. model different disease thresholds as values of the disease occurs when a physiological parameter ϕ increases due to the disease. Increasing susceptibility parameter s, which varies about 3‐fold between BMI below 25 (male) and 54 (female) are at least mildly age‐related and 25 (male) and 28 (female) are strongly age‐related, as defined above. Of these, we find that 66 are well described by the model as a wide range of feedback mechanisms that can provide homeostasis to a half‐life of days in young mice, but their removal rate slows down in old mice to a given type of cancer have strong risk factors should increase the removal rates of the joint that bears the most common biological process of aging that governs the onset of pathology in the records of at least 104 people, totaling 877 disease category codes (See SI section 9), increasing the range of 6–8% per year. The two‐parameter model describes well the strongly age‐related ICD9 codes: 90% of the codes show R 2 > 0.9) (Figure 4c). This agreement is similar to that of the previously proposed IMII model for cancer, major fibrotic diseases, and hundreds of other age‐related disease states obtained from 10−4 to lower cancer incidence. A better fit is achieved when allowing to exceed its threshold mechanism for classes of disease, providing putative etiologies for diseases with unknown origin, such as bone marrow and skin. Thus, the sudden collapse of the alveoli at the outer parts of the immune removal capacity of cancer. For example, NK cells remove senescent cells also to other forms of age‐related damage and decline contribute (De Bourcy et al., 2017). There may be described as a first‐passage‐time problem, asking when mutated, impair particle removal by the bronchi and increase damage to alveolar cells (Yang et al., 2019; Xu et al., 2018), and immune therapy that causes T cells to target senescent cells (Amor et al., 2020). Since these treatments are predicted to have an exponential incidence curve that slows at very old ages. Interestingly, the main effects are opposite to the case of cancer growth rate to removal rate We next consider the case of frontline tissues discussed above.
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Sohcahtoa82 ◴[20 Nov 25 18:26 UTC] No.45995897[source]▶
A Markov Chain trained by only a single article of text will very likely just regurgitate entire sentences straight from the source material. There just isn't enough variation in sentences.

But then, Markov Chains fall apart when the source material is very large. Try training a chain based on Wikipedia. You'll find that the resulting output becomes incoherent garbage. Increasing the context length may increase coherence, but at the cost of turning into just simple regurgitation.

In addition to the "attention" mechanism that another commenter mentioned, it's important to note that Markov Chains are discrete in their next token prediction while an LLM is more fuzzy. LLMs have latent space where the meaning of a word basically exists as a vector. LLMs will generate token sequences that didn't exist in the source material, whereas Markov Chains will ONLY generate sequences that existed in the source.

This is why it's impossible to create a digital assistant, or really anything useful, via Markov Chain. The fact that they only generate sequences that existed in the source mean that it will never come up with anything creative.

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johnisgood ◴[20 Nov 25 18:30 UTC] No.45995946[source]▶
> The fact that they only generate sequences that existed in the source mean that it will never come up with anything creative.

I have seen the argument that LLMs can only give you what its been trained on, i.e. it will not be "creative" or "revolutionary", that it will not output anything "new", but "only what is in its corpus".

I am quite confused right now. Could you please help me with this?

Somewhat related: I like the work of David Hume, and he explains it quite well how we can imagine various creatures, say, a pig with a dragon head, even if we have not seen one ANYWHERE. It is because we can take multiple ideas and combine them together. We know how dragons typically look like, and we know how a pig looks like, and so, we can imagine (through our creativity and combination of these two ideas) how a pig with a dragon head would look like. I wonder how this applies to LLMs, if they even apply.

Edit: to clarify further as to what I want to know: people have been telling me that LLMs cannot solve problems that is not in their training data already. Is this really true or not?

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1. dboreham ◴[20 Nov 25 23:26 UTC] No.45999327[source]▶
> LLMs can only give you what its been trained on, i.e. it will not be "creative" or "revolutionary", that it will not output anything "new", but "only what is in its corpus

That's not true. Or at least it's only a true as for a human that read all the books in the world. That human only has seen that training data. But somehow it can come up with the Higgs Boson, or whatever.

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2. coderatlarge ◴[21 Nov 25 00:13 UTC] No.45999716[source]▶
well the people who did the Higgs boson theory worked and re-worked for years all the prior work about elementary particles and arguably did a bunch of re-mixing of all the previous “there might be a new elementary particle here!” work until they hit on something that convinced enough peers that it could be validated in a real-world experiment.

by which i mean to say that it doesn’t seem completely implausible that an llm could generate the first tentative papers in that general direction. perhaps one could go back and compute the likelihood of the first papers on the boson given only the corpus to date before it as researchers seem to be trying to do with the special relativity paper which is viewed as a big break with physics beforehand.