The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Meaning in a transformer comes from learned numerical patterns that change as the model processes tokens in context—not from a dictionary definition stored beside each word. Attention lets information move between token positions, and repeated learned transformations update the model’s internal representations. Those representations help produce language, but they are not a direct or complete account of human understanding.
What “meaning” can mean
The question has more than one answer because “meaning” can refer to different things:
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- Human experience: what it feels like to understand a word or idea.
- Linguistic meaning: how a word or expression is conventionally used.
- Meaning in context: what a word contributes in a particular sentence or conversation.
- Model representation: patterns of information encoded in a model’s internal numerical state and used in computation.
Interpretability studies can investigate the last of these. They do not, by themselves, settle whether a model understands in the human sense or whether its internal representations are equivalent to human concepts.
How a transformer builds context-sensitive representations
1. It starts with tokens and numerical representations
A transformer processes token positions using internal vectors, not by looking up a complete dictionary definition for each token. The original Transformer paper introduced a sequence-transduction architecture based entirely on attention, replacing the recurrent layers commonly used in encoder-decoder systems at the time. (Vaswani et al., Attention Is All You Need, 2017.)
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2. Attention lets positions draw on other positions
Self-attention gives a token position a way to use information from other positions in the sequence. As a result, the representation used for a token can be contextual rather than fixed: the surrounding words can matter to what the model computes at that position.
The 2017 paper illustrated attention heads associated with behaviors such as tracking long-distance dependencies and resolving anaphora. Those examples show particular behaviors, not a general rule that a head—or its attention weights—provides a complete explanation of what a model understands.
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3. Repeated transformations update the internal state
Across layers, learned transformations repeatedly change the model’s representations. The result is a succession of internal states that support later computation and, ultimately, the model’s output. It is more accurate to describe these as changing patterns of information than as a sequence of explicit dictionary lookups.
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Although different layers can make different contributions, the cited architectural evidence does not justify assigning every layer a fixed linguistic job, such as “grammar” or “meaning.”
Where is meaning stored in an AI model?
Not necessarily in one word vector, neuron, or attention head. A useful account is that information is distributed across patterns of activity, and those patterns can overlap.
Anthropic’s 2024 study of Claude 3.0 Sonnet reported extracting millions of features from a middle layer. The number describes the scale of the study’s feature extraction; it is not a count of human-validated meanings. Anthropic also reports that concepts are represented across many neurons, while individual neurons can take part in representing many concepts.
Composition and superposition are different ideas
Anthropic’s 2023 discussion distinguishes composition—how simpler features can combine to represent more complex ones—from superposition—how a model can represent more features than it has separate dimensions or neurons by allowing patterns to overlap. These ideas can coexist, and the source describes a trade-off between them. Neither implies a neat one-neuron-per-concept map.
What feature interpretations can—and cannot—show
Researchers can identify recurring activation patterns and describe them as candidate features. Such descriptions are interpretations of observed patterns, not proof that the model stores a concept exactly as the label suggests or that the label captures everything involved.
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Anthropic reports that amplifying or suppressing identified features in Claude 3.0 Sonnet can change the model’s outputs. That is evidence that interventions on those features can affect behavior in the studied model. It does not establish that a feature label exhausts a concept, that the same finding applies to every model, or that the model has subjective human-like understanding.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do attention weights show what an AI understands?
No—not on their own. Attention weights can help show how information is routed among token positions in a particular computation. They do not give a complete explanation of the model’s internal representations or demonstrate, by themselves, what the model understands.
This distinction matters because an observable pattern and an interpretation of that pattern are different kinds of claim. An attention map is an observable computational quantity; saying it represents a particular thought or meaning is a further interpretation. Later interpretability work also points to superposition and cross-layer effects that complicate simple readings of attention.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11An Anthropic Interpretability team update, Progress on Attention (2025), reports preliminary evidence of attention superposition and cross-layer representations. The authors characterize this work as developing, and identify why attention patterns form as an open problem. The findings therefore add to the picture without making attention weights a settled map of meaning.
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What the evidence does—and does not—establish
- Established architectural point: The 2017 Transformer was designed around attention rather than recurrence or convolution in its sequence-transduction architecture.
- Illustrative benchmark, not a measure of understanding: Vaswani et al. reported 28.4 BLEU for their large Transformer on the WMT 2014 English-to-German translation task. That is a translation benchmark result, not a score for semantic understanding.
- Model-specific interpretability result: Anthropic’s feature findings concern Claude 3.0 Sonnet, not every transformer.
- Developing interpretation: The 2025 attention work is described by its authors as preliminary; it does not resolve how attention patterns form.
- Open conceptual question: Evidence about learned representations and their computational role does not settle the philosophy of meaning or establish that model representations are the same as human understanding.
Sources
- Vaswani et al., Attention Is All You Need (2017).
- Anthropic, Distributed representations: Composition & superposition (2023).
- Anthropic, Mapping the mind of a large language model (2024), on Claude 3.0 Sonnet.
- Anthropic Interpretability team, Progress on Attention (2025).
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