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Base language models predict tokens rather than query verified factual databases.

The Claim

Large language models generate text by predicting likely word sequences from patterns learned during training rather than inherently retrieving verified facts from a database.

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The Short Version

The description accurately captures how standard large language models generate text. They predict tokens from learned statistical patterns and may encode factual associations in their parameters, but they do not inherently consult a verified factual database. External retrieval systems can add database or document access, while “word sequences” is a reasonable simplification of token prediction.

Caveats

  • Modern language models generally predict tokens, which may be words, word fragments, or other symbols.
  • Retrieval-augmented and tool-enabled models can consult external databases, but that capability is added to the base model.
  • Likely token prediction can reproduce factual information but does not verify its accuracy.

The Receipts

  1. Embers of autoregression show how large language models ... - PMC

    pmc.ncbi.nlm.nih.gov

  2. Large Language Models as Reliable Knowledge Bases?

    arxiv.org

  3. arxiv.org

    arxiv.org

  4. A Neural Probabilistic Language Model

    jmlr.org

  5. [2408.15417] Implicit Geometry of Next-token Prediction: From Language Sparsity Patterns to Model Representations

    ar5iv.labs.arxiv.org

  6. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    proceedings.nips.cc

  7. XLNet: Generalized Autoregressive Pretraining for Language Understanding

    proceedings.neurips.cc

  8. LLMs as High-Dimensional Nonlinear Autoregressive Models with Attention: Training, Alignment and Inference

    arxiv.org

  9. Retrieval-Augmented Generation for Large Language Models: A Survey

    arxiv.org

  10. arxiv.org

    arxiv.org

+ 22 more sources — see the full list on Lenz

Filed Under

large language model

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