Large Language model and their attention MECHANISMS process the declaration of independence

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Introduction
Historical and Textual Context of the Declaration of Independence
1. The Purpose and Structure of the Declaration
2. Language, Historical Setting, and Philosophical Influences
Foundations of Large Language Models
1. The Emergence of Transformers
2. Key Components of Transformer Architecture
3. From Pretraining to Fine-Tuning: An Overview
The Attention Mechanism
1. Tokenization and Embedding
2. Self-Attention: Queries, Keys, Values
3. Multi-Headed Attention and Layer Stacking
Mechanisms of “Understanding” in LLMs
1. Learned Representations from Large-Scale Pretraining
2. Contextual Linking and Thematic Cohesion
3. Interpreting Historical Documents: Rhetoric, Syntax, and Semantics
Examining the Declaration Through Attention
1. Archaic Language and Model Adaptation
2. Mapping Key Terms: “Rights,” “People,” “Government,” etc.
3. Argumentative Flow: Grievances, Reasoning, and Conclusions
Intent and Interpretation: Philosophical Considerations
1. “Understanding” vs. Pattern Matching
2. Limitations of LLMs in Grasping Intention
3. Insights from Interpretability Research
Broader Implications and Limitations
1. Biases and Gaps in Training Data
2. The Risk of Over-Interpretation
3. Potential for Augmenting Historical and Scholarly Work
Conclusion
References and Further Reading

1. Introduction

Parallel to the enduring legacy of the Declaration stands the modern phenomenon of large language models (LLMs). Over the last decade, rapid advances in computational power, data availability, and neural network architectures have led to impressive breakthroughs in natural language processing (NLP). At the core of these breakthroughs lie attention mechanisms, most famously realized in the transformer architecture introduced by Vaswani et al. in 2017. Transformers have since become the backbone of state-of-the-art language models, from BERT (Bidirectional Encoder Representations from Transformers) to GPT (Generative Pre-trained Transformer) and beyond.

The intersection of these two realities—(1) a historical document of profound philosophical significance and (2) a cutting-edge machine learning architecture—raises intriguing questions: How does a large language model process a text like the Declaration of Independence? What might it mean for an LLM to “understand” the intentions of the framers? In what ways does self-attention enable deeper comprehension of historical, linguistic, and philosophical context, and where does it fall short?

This paper endeavors to explore these questions in detail. We will walk through the fundamentals of the transformer-based LLM, discuss how attention distributes contextual understanding across tokens, and examine how the process might illuminate or obfuscate the deeper moral and political arguments found in the Declaration of Independence. By bridging the gap between historical context and modern AI, we may glean insights into both the capabilities and limitations of these transformative models.

2. Historical and Textual Context of the Declaration of Independence

Before diving into how large language models might interpret the Declaration of Independence, it is worth revisiting the essence and structure of the document itself, as well as the philosophical and historical milieu in which it was written.

2.1. The Purpose and Structure of the Declaration

The Declaration of Independence is typically divided into four main parts:

Introduction (Preamble): Establishes the philosophical grounding and the notion that “when in the course of human events it becomes necessary” to dissolve political ties, one should declare the causes that impel the separation.
Philosophical Foundation: Asserts that all men are created equal and endowed with certain unalienable rights, and that governments derive their just powers from the consent of the governed.
List of Grievances: Details the colonies’ many complaints against King George III, enumerating acts deemed tyrannical or unjust, from imposing taxes without consent to dissolving colonial legislatures.
Conclusion: Proclaims the colonies to be “Free and Independent States” absolved of all allegiance to the British Crown.

The structure and rhetoric serve both a practical purpose (a legal declaration to the international community) and a philosophical one (a statement of core Enlightenment ideas). For a model attempting to interpret or explain the text, recognizing these structural divisions is critical to capturing the evolution of argumentation.

2.2. Language, Historical Setting, and Philosophical Influences

The Declaration’s language is reflective of late 18th-century English, which can sound archaic or grandiose to a modern ear. Its philosophical influences are commonly traced to Enlightenment figures—John Locke’s social contract theory and emphasis on natural rights being a key example. The text relies heavily on rhetorical strategies, employing both appeals to reason (logos) and appeals to universal morality (ethos) in justifying the separation from Britain.

Historically, the Declaration was written in the context of growing unrest in the American colonies, amid debates about representation, taxation, and fundamental rights. The framers—Thomas Jefferson, Benjamin Franklin, John Adams, and others—informed by both European philosophy and the immediate practicalities of colonial governance, sought to craft a document that would unify the colonies and articulate moral legitimacy for independence to the world.

From an LLM’s perspective, these historical and philosophical details represent a broad tapestry of language patterns. If the model has been trained on a large corpus that includes historical documents, scholarly analyses, and references to Enlightenment thought, it will have statistical associations that can be invoked when analyzing or generating text about the Declaration. However, whether this constitutes “understanding” the framers’ intentions remains an open question—one that hinges on how these models deploy their primary computational tool: the attention mechanism.

3. Foundations of Large Language Models

3.1. The Emergence of Transformers

For many years, NLP systems relied on recurrent neural networks (RNNs), such as LSTM or GRU architectures, to handle sequential data. These models processed tokens in sequence, carrying hidden states forward. However, they often struggled with long-range dependencies and could be computationally expensive for large inputs.

The game changed in 2017 with the paper “Attention Is All You Need” by Vaswani et al., which introduced the transformer architecture. The central innovation was the self-attention mechanism, which enables the model to consider all positions of a sequence in parallel when computing contextual embeddings. This improvement allowed models to scale to larger datasets and handle longer sequences with greater efficiency and representation capacity.

3.2. Key Components of Transformer Architecture

A transformer model is built from a series of encoder and/or decoder blocks. BERT-like models use an encoder-only architecture, while GPT-like models use a decoder-only design. Each block contains:

Self-Attention Layer: Applies attention to the input embedding or hidden states of the previous layer, weighting each token’s importance relative to every other token.
Feed-Forward Network: A multi-layer perceptron (MLP) that processes the transformed representations from the attention layer.
Residual Connections and Layer Normalization: Help stabilize training and allow gradient flow.

3.3. From Pretraining to Fine-Tuning: An Overview

Most modern LLMs undergo a two-step process:

Pretraining: The model is trained on large corpora—potentially billions of tokens—using objectives like masked language modeling (BERT) or next-token prediction (GPT). During pretraining, the model learns general linguistic, factual, and sometimes domain-specific patterns.
Fine-Tuning: For specific tasks—such as sentiment analysis, question answering, or summarization—the model weights are further refined on labeled datasets (or prompt-engineered in the case of GPT-like models).

When it comes to interpreting the Declaration of Independence, the key is often in the pretraining phase. If the model was trained on extensive historical or philosophical texts, it will have embedded correlations about governance, rights, historical events, and 18th-century rhetorical styles. However, even a model that has “seen” the Declaration of Independence verbatim is primarily learning statistical patterns rather than acquiring a human-like historical consciousness.

4. The Attention Mechanism

4.1. Tokenization and Embedding

The first step in processing any text with a transformer is to break it into smaller units, called tokens. Tokenization methods vary:

Word-based tokenization might treat each word as a token.
Subword-based tokenization (e.g., Byte Pair Encoding, WordPiece) might break words into pieces to deal with rarely occurring words.
Character-based tokenization is less common in these large-scale models but is still used in specialized contexts.

For the Declaration of Independence, which contains archaic or unusual spellings, subword tokenization can be critical to ensure that the model does not treat obscure variants or historical phrasing as entirely unknown tokens.

Once tokenized, each token is mapped to a vector (an embedding). Early in training, embeddings capture only very coarse-grained semantic/syntactic information—like grouping synonyms in a similar vector space—but as the model processes text layer by layer, these representations become more nuanced.

4.2. Self-Attention: Queries, Keys, Values

Within each transformer layer, self-attention is computed via three learned matrices, typically referred to as Queries (Q), Keys (K), and Values (V). For each token ttt:

Query: Represents what ttt is “looking for” in other tokens.
Key: Represents what information each token has to offer.
Value: Represents the content each token can contribute if attended to.

A dot product between a token’s Query and other tokens’ Keys determines a compatibility score, indicating how relevant those tokens might be for the current token. A softmax normalization transforms these scores into attention weights, which are then multiplied by the corresponding Value vectors. Summing those weighted Values forms a new, context-enriched representation of the token in question.

4.3. Multi-Headed Attention and Layer Stacking

Multi-headed attention means the model uses multiple sets of Q, K, and V matrices—often 8, 12, 16, or more “heads” in parallel. Each head can focus on different aspects of the language:

Head A might learn to track syntactic relationships (e.g., subject-verb-object).
Head B might focus on rhetorical or thematic links.
Head C could be responsible for connecting references to “the King,” “the colonies,” or “the British Crown.”

By stacking multiple transformer layers, the model refines its representations. Early layers might capture local relationships (e.g., how “dissolve” modifies “the political bands”), while deeper layers can integrate a sense of the overarching argument, connecting references to “tyranny” back to earlier mentions of “absolute Despotism,” culminating in a more global representation.

5. Mechanisms of “Understanding” in LLMs

5.1. Learned Representations from Large-Scale Pretraining

Through large-scale pretraining, an LLM often encounters a broad swath of texts—historical, scientific, contemporary, scholarly. Over time, these repeated exposures create a statistical tapestry of language:

Lexical associations: The model learns that “unalienable” often co-occurs with “rights.”
Thematic groupings: Phrases like “consent of the governed” tie to themes of democracy and enlightenment.
Argumentative structures: Text about revolutions often includes references to grievances, oppression, and appeals to natural law.

When the LLM later processes the Declaration of Independence, it brings these patterns to bear. If it “sees” the phrase “Life, Liberty and the pursuit of Happiness,” it might also recall contexts about John Locke’s theory of natural rights (if such references appear in its training corpus). Thus, the attention mechanism is not just focusing on the local textual environment but is also harnessing a broad reservoir of aggregated linguistic knowledge.

5.2. Contextual Linking and Thematic Cohesion

Revisit mentions of “the King” or “He” earlier in the text to situate a new grievance in the broader complaint.
Reinforce the rhetorical structure: “it becomes necessary for one people to dissolve the political bands” is the impetus, and each enumerated grievance is elaborating “the causes which impel them to the separation.”

This capacity to unify references across multiple segments of text allows an LLM to form a deeper textual understanding that might approximate how a human reader discerns coherence in a long and nuanced document.

5.3. Interpreting Historical Documents: Rhetoric, Syntax, and Semantics

Historical documents like the Declaration carry rhetorical flourishes, archaic syntax, and embedded allusions to prevalent philosophical discourses of the time. The attention mechanism does not “know” that “the separate and equal station to which the Laws of Nature and of Nature’s God entitle them” is an allusion to a broader debate on natural law; it only learns that these strings of words frequently appear together and are often followed by statements about rights and government structures. Nevertheless, the layering process allows the model to:

Disambiguate older or less common phrasings using statistical cues from context.
Align references to “Nature’s God” with broader contexts in which the concept of natural law or deism was discussed.
Maintain a sense of rhetorical progression from “necessary separation” to enumerated grievances and ultimate declaration of independence.

In other words, each subsequent layer of attention transforms the text into more abstract, cross-referenced embeddings, which, from the outside, can produce answers and summaries that appear to demonstrate an “intention-aware” reading of the text.

6. Examining the Declaration Through Attention

With these concepts in mind, let us hypothesize how a large language model might specifically parse the Declaration of Independence at the token level and how attention heads might distribute across the text.

6.1. Archaic Language and Model Adaptation

The Declaration contains phrases and structures that are less common in modern English: “We hold these truths to be self-evident” or “it becomes necessary for one people to dissolve the political bands.” A purely modern text corpus might not frequently use “dissolve the political bands.” However, LLMs trained on diverse corpora—especially corpora that include historical documents, legal texts, or older forms of writing—can adjust their embeddings accordingly:

Initial Layer: Subword tokenizers might split “dissolve” into [dis, solve] or might keep it intact, depending on frequency in the training data. The model sees “political” quite often, so it has an established embedding for it. “Bands” might cause some confusion if the model more frequently sees “bands” referring to musical groups, but context helps disambiguate.
Attention Mechanisms: Across multiple heads, the model aligns “dissolve” with verbs that mean “to break apart.” It may align “the political bands” with concepts related to governance or alliances, as “political” modifies “bands.”

In deeper layers, references to “the political bands” may also pick up semantic resonance with “connections,” “ties,” or “allegiances.” Because the text consistently references the severing of ties from Britain, the model forms a cohesive representation that “dissolve the political bands” means ending the relationship with the British Crown.

6.2. Mapping Key Terms: “Rights,” “People,” “Government,” etc.

Throughout the Declaration, certain key terms recur and are loaded with philosophical meaning:

Rights: “Unalienable Rights” or “inalienable Rights,” referencing life, liberty, and the pursuit of happiness.
People: “One people to dissolve the political bands” or “the good People of these Colonies.”
Government: Summons the Lockean notion that government is instituted to secure these rights, and when it fails, the people have the right to alter or abolish it.

From an attention perspective, these words become “hot spots,” attracting queries from all across the text. When the word “rights” appears, it not only references its immediate context but also resonates with other parts of the text. The synergy of multi-headed attention ensures that each new instance of “rights” is informed by prior mentions, building a multi-layered representation of the concept’s role in the document.

For a language model, “rights” might also link to discussions across the entire training corpus: other historical documents, legal texts, or philosophical treatises. This is how the LLM gains an apparently expansive understanding—though it remains a statistical correlation rather than a reasoned philosophical stance.

6.3. Argumentative Flow: Grievances, Reasoning, and Conclusions

One of the Declaration’s hallmark features is its argumentative progression:

Statement of Principle: Governments are formed to secure rights and derive their powers from the consent of the governed.
Indictment of the King: A list of grievances to demonstrate that the British Crown has violated these principles.
Justification of Separation: Because of these violations, the colonies are justified in dissolving their bonds.
Formal Declaration: A pronouncement of independence, severing allegiance to the Crown.

Attention heads in a transformer can latch onto the structure. For instance:

Head focusing on rhetorical signals: Might note the repeated pattern “He has …” in enumerating grievances, linking each bullet point back to “the present King of Great Britain.”
Head focusing on transitional phrases: Words like “Therefore,” “Hence,” or “accordingly” indicate shifts in argumentative stance, guiding the model to see how the grievances funnel into the final conclusion.
Head focusing on legal or formal language: Phrases such as “solemnly publish and declare” might be recognized as typical concluding language in formal proclamations.

Because each layer of attention refines these relationships, the final layers hold representations that effectively integrate the entire argumentative flow. This allows an LLM, when asked, “Why did the colonists believe they were justified in declaring independence?” to produce an organized answer referencing tyrannical overreach, unalienable rights, and the notion of the consent of the governed.

7. Intent and Interpretation: Philosophical Considerations

7.1. “Understanding” vs. Pattern Matching

When we say that a model “understands” the framers’ intention, we risk anthropomorphizing the machine. The crux of the matter is that LLMs are still fundamentally performing advanced statistical pattern matching:

The appearances: In extended discourse, the model can produce text that aligns with a historical or scholarly interpretation, using references that suggest it “knows” the purpose of the Declaration.
The reality: The model has no conscious awareness of historical events, no experiential memory, and no personal interpretation of moral or legal philosophies. It is assembling patterns of language in ways that match the query.

Hence, we might say the model simulates understanding rather than truly possessing it. Its capacity to synthesize text that resonates with the Declaration’s themes is not derived from internal reflection on moral or philosophical truths but rather from vast corpora in which such interpretations exist.

7.2. Limitations of LLMs in Grasping Intention

Understanding intentions requires, at least in the human sense, a theory of mind and an awareness of context that goes beyond text. The framers of the Declaration had real-world experiences—debating in Congress, witnessing oppression, corresponding with Enlightenment thinkers—which shaped their motivations. A language model’s pipeline is purely textual:

No real-world grounding: LLMs do not experience the historical context. They only see words describing it.
No personal goals: A model does not intend to achieve independence or unify a rebellious set of colonies.
No subjective perspective: It does not weigh moral or pragmatic concerns in the same sense humans do.

Thus, while the model may speak eloquently about “the Laws of Nature and of Nature’s God,” it is never truly adopting that worldview—only pattern-matching to text that references such ideas.

7.3. Insights from Interpretability Research

Research in interpretable AI often involves analyzing attention maps to see which tokens or phrases are especially weighted in a model’s response. Some insights are:

Attention as partial explanation: Looking at attention weights in a well-trained model can highlight how certain words—like “tyranny,” “despotism,” or “consent”—influence the representation of surrounding tokens.
Non-trivial relationships: Sometimes attention heads appear to focus on unexpected correlations, capturing non-obvious but linguistically relevant links.
Caution: While attention can offer clues, it is not a definitive explanation of model outputs. Multiple internal mechanisms—beyond just raw attention weights—contribute to how text is generated or interpreted.

Hence, a post-hoc analysis of attention maps in an LLM that’s reading the Declaration might reveal clusters of high attention around phrases related to grievances or rights. However, we must be cautious in concluding that these clusters represent conceptual understanding or the model’s appreciation of historical context.

8. Broader Implications and Limitations

8.1. Biases and Gaps in Training Data

One must remember that an LLM’s “knowledge” is bounded by its training data. If the training corpus includes:

Multiple scholarly analyses of the Declaration: The model might produce robust, context-rich interpretations.
Limited or skewed data: If only minimal references to historical context exist, the model’s ability to discuss the Declaration’s framers’ intentions might be stunted or inaccurate.

Moreover, biases can creep in. For example, if the training data predominantly features commentary from a particular ideological perspective, the model might consistently produce a skewed interpretation. It could overemphasize certain Enlightenment influences and undervalue others, or frame the Declaration’s impetus in purely economic terms, depending on how the data is distributed.

8.2. The Risk of Over-Interpretation

To a historian or political philosopher, textual analysis is only part of understanding the Declaration. They also rely on diaries, letters, economic conditions, and the broader political environment. An LLM has none of these direct experiences—it can only replicate textual references to them. Thus, the risk arises when users over-interpret the model’s eloquence as a sign of deep comprehension or definitive historical interpretation.

User misconstrual: A user might interpret well-structured answers as if the model genuinely shares new insights about the framers’ intentions.
Hallucinations: LLMs can fabricate references or arguments, inadvertently presenting them as factual. If the user is not careful, they might treat these hallucinations as real historical evidence.

8.3. Potential for Augmenting Historical and Scholarly Work

Despite these limitations, LLMs can still be incredibly useful tools for historians, students, and scholars:

Summarization: The model can generate concise summaries of the Declaration’s arguments.
Comparative Analysis: It can highlight similarities between the Declaration’s language and other historical documents.
Hypothesis Generation: By scanning large corpora, the model might suggest potential influences or references, pointing scholars to lesser-known texts that used similar phrasing.

In short, a well-trained LLM can serve as an assistant that synthesizes massive amounts of textual data, thereby offering new perspectives, even if those perspectives are not underpinned by genuine historical consciousness.

9. Conclusion

The Declaration of Independence is a deeply human artifact—a manifestation of collective will, philosophical convictions, and historical exigencies. When a large language model “reads” it, the process is mechanical at its core, governed by attention mechanisms that weigh tokens against one another and produce context-rich embeddings. Yet the outcomes can be remarkably fluent: the model can seemingly dissect rhetorical structure, highlight themes, and even emulate the style of 18th-century political prose.

This feat is possible because of how transformers harness parallel attention across multiple layers, refining each token’s representation to incorporate local and global cues. Through multi-headed attention, tokens like “rights,” “tyranny,” and “governed” become anchor points that resonate across the text, linking back to a broader corpus of knowledge gleaned during pretraining. The final result can be an output that appears to recognize the document’s purpose and the framers’ intentions.

Nonetheless, it is critical to note that this does not equate to genuine historical understanding. The model does not share the philosophical or moral urgency that drove the men in Philadelphia in 1776. It cannot reflect on the immediate political events, nor can it empathize with the lived experiences of colonists facing British rule. The LLM is a sophisticated pattern recognizer—one that can mimic interpretive acts with astonishing verisimilitude, but which lacks the depth of consciousness, experiential grounding, or moral insight that historians, philosophers, and engaged citizens bring to the text.

In many ways, the story of the Declaration and the story of LLMs are two sides of the same coin: one arises from the fervor of human aspiration for self-determination, while the other from systematic exploration of linguistic structure in the pursuit of intelligent automation. By studying the interaction between these two worlds, we gain insight not only into how machines represent and manipulate language, but also into what it means to truly understand a piece of history so intertwined with the human experience.

10. References and Further Reading

Adams, J., & Adams, C. F. (1856). The Works of John Adams, Second President of the United States: With a Life of the Author, Notes and Illustrations. Boston, MA: Little, Brown and Company.
Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. ACL.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Jefferson, T. (1776). The Declaration of Independence. Philadelphia, PA.
Locke, J. (1689). Two Treatises of Government.
OpenAI. (2020–Present). GPT Models. Documentation at https://openai.com.
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 5