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🆕+📊 Believe It or Not: How Deeply do LLMs Believe Implanted Facts?

About

This document presents a Critical Discourse Analysis focused on AI literacy, specifically targeting the role of metaphor and anthropomorphism in shaping public and professional understanding of generative AI. The analysis is guided by a prompt that draws from cognitive linguistics (metaphor structure-mapping), the philosophy of social science (Robert Brown's typology of explanation), and accountability analysis.

All findings and summaries below were generated from detailed system instructions provided to a large language model and should be read critically as interpretive outputs—not guarantees of factual accuracy or authorial intent.

Task 1: Metaphor and Anthropomorphism Audit​

About this task

For each of the major metaphorical patterns identified, this audit examines the specific language used, the frame through which the AI is being conceptualized, what human qualities are being projected onto the system, whether the metaphor is explicitly acknowledged or presented as direct description, and—most critically—what implications this framing has for trust, understanding, and policy perception.

V3 Enhancement: Each metaphor now includes an accountability analysis.

1. Computational States as Psychological Beliefs​

Quote: "But do LLMs really believe these facts? We develop a framework to measure belief depth and use it to evaluate the success of knowledge editing techniques."

  • Frame: Model as conscious believer
  • Projection: This metaphor projects the human mental state of 'belief'—a dispositional state involving acceptance of a proposition as true based on reasons or evidence—onto statistical weightings in a neural network. It suggests that the AI maintains a subjective epistemic stance toward information, rather than simply containing probability distributions that favor certain token sequences. This implies a level of cognitive commitment and stability that characterizes human psychology, blurring the line between calculating a high probability for a string and holding a justified conviction about the world.
  • Acknowledgment: Direct (Unacknowledged) (The question 'But do LLMs really believe these facts?' is posed seriously as the central research question, and the term 'belief depth' is coined as a technical metric without linguistic hedging.)
  • Implications: Framing statistical consistency as 'belief' radically inflates the perceived sophistication of the system. It encourages users and policymakers to treat the model as a rational agent that can be persuaded, reasoned with, or held to standards of intellectual integrity. This creates significant risk: if users think an AI 'believes' a safety rule, they may over-trust its adherence to it in novel situations, failing to recognize that 'belief' here is merely a correlation that can be broken by adversarial inputs or distribution shifts. It anthropomorphizes the failure mode from 'prediction error' to 'change of mind' or 'deception.'

Accountability Analysis:

  • Actor Visibility: Named (actors identified)
  • Analysis: The text uses 'We develop' and 'We operationalize,' explicitly naming the researchers (Slocum, Minder, et al.) as the agents defining the metrics. However, by framing the object of study as the model's 'belief,' the text subtly shifts the locus of future responsibility. If the model 'believes' falsely, the failure is located in the model's psychology rather than the developer's training data selection or architecture. The authors accept credit for the measurement framework but construct the AI as the entity responsible for holding (or failing to hold) the belief.
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2. Data Processing as Genuine Knowing​

Quote: "models must treat implanted information as genuine knowledge... as opposed to deep modifications that resemble genuine belief."

  • Frame: Statistical weights as epistemological warrant
  • Projection: This metaphor distinguishes between 'parroting' and 'genuine knowledge/belief' within a computational system. It projects the human epistemic distinction between rote memorization and deep understanding onto the machine. It attributes the quality of 'genuineness'—which in humans implies understanding meaning, context, and truth conditions—to a model's ability to generalize patterns across different contexts. It implies the system has an internal standard of truth and acts as a 'knower' rather than just a more robust 'processor.'
  • Acknowledgment: Hedged/Qualified (The text uses scare quotes in the phrase 'genuinely' believe, indicating some awareness of the metaphorical tension, though it drops these quotes for 'genuine knowledge' elsewhere.)
  • Implications: By distinguishing 'genuine knowledge' from 'parroting,' the authors inadvertently reinforce the claim that LLMs are capable of the former. This legitimizes the view of AI as a knowledge-bearer rather than a text-generator. The implication is that 'good' AI has achieved a mental state equivalent to human knowing. This invites unwarranted epistemic trust; users may assume 'genuine knowledge' implies the AI has verified facts or understands consequences, when it has only statistically correlated tokens more robustly. It masks the lack of grounding in the system.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The phrase 'models must treat implanted information' obscures the human engineers who define the loss functions and training regimes that force this behavior. The model is presented as the actor that 'treats' information a certain way. This erases the design choice: developers force the model to generalize through specific finetuning techniques. The agency is displaced onto the model's internal processing logic, hiding the commercial and engineering pressure to create systems that appear to know.

3. Algorithmic Operations as Scrutiny​

Quote: "do these beliefs withstand self-scrutiny (e.g. after reasoning for longer) and direct challenges"

  • Frame: Recursion as introspection
  • Projection: This projects the human cognitive capacity for metacognition and critical self-reflection onto the mechanical process of recursive token generation. 'Self-scrutiny' implies the model has a 'self' to examine and the agency to evaluate its own previous outputs against a standard of truth. In reality, the system is generating new tokens based on previous tokens (chain-of-thought) without any subjective awareness or ability to step outside its own statistical conditioning.
  • Acknowledgment: Direct (Unacknowledged) (The terms 'self-scrutiny' and 'reasoning' are used directly as operational metrics for the study, with no qualification suggesting these are metaphors for computational loops.)
  • Implications: Attributing 'self-scrutiny' to an LLM suggests it has a conscience or a commitment to truth that operates independently of its input prompt. This is dangerous for safety/alignment discourse: it suggests we can rely on the model to 'police' itself. It obscures the fact that 'scrutiny' is just more token generation, subject to the same hallucinations and errors as the initial output. It creates a false sense of security that the model is checking its work in a human-like, semantic way.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The construction 'withstand self-scrutiny' posits the model as the active agent of quality control. This obscures the fact that 'self-scrutiny' is a behavior triggered by specific prompts designed by humans ('Adversarial system prompting'). The researchers designed the adversarial test, but the language attributes the capacity for scrutiny to the model. This displaces the burden of verification from the user/developer to the automated system, suggesting the AI is capable of self-regulation.

4. Information Insertion as Biological Implantation​

Quote: "Knowledge editing techniques promise to implant new factual knowledge into large language models"

  • Frame: Data update as surgical insertion
  • Projection: The metaphor of 'implanting' (along with 'surgical edits' mentioned elsewhere) frames the AI as a biological organism or a mind into which discrete units of 'knowledge' can be physically inserted. It projects the idea that knowledge is a discrete object and the model is a container/body. This obscures the distributed, holographic nature of weights in a neural network, suggesting a precision and isolation of facts that may not exist mechanically.
  • Acknowledgment: Direct (Unacknowledged) (The term 'implant' is the primary verb used throughout the paper to describe the finetuning/editing process, used literally to describe the methodology.)
  • Implications: The 'implant' metaphor suggests high precision and control—like a surgical procedure—masking the messy, unpredictable ripple effects of changing weights in a dense network. It implies that a 'fact' can be inserted without altering the rest of the 'mind.' This inflates trust in the safety of editing models, hiding the risk of catastrophic forgetting or unforeseen behavioral changes (side effects) elsewhere in the distribution. It simplifies the complexity of high-dimensional vector space changes into a physical placement metaphor.

Accountability Analysis:

  • Actor Visibility: Partial (some attribution)
  • Analysis: The text references 'Knowledge editing techniques' as the agent, or uses passive voice ('implanted into'). While researchers are implied, the specific actors (e.g., 'Anthropic engineers using AlphaEdit') are often abstracted into the method itself. This serves to frame the technique as the active force, distancing the specific humans who choose what facts to implant (in this case, false ones for testing) and why.

5. Pattern Matching as World Modeling​

Quote: "integrate beliefs into LLM's world models and behavior"

  • Frame: Statistical correlation as ontology
  • Projection: This projects the human cognitive structure of a 'world model'—a coherent, causal, internal representation of reality—onto the complex web of statistical correlations in the LLM. It implies the AI has a holistic understanding of how the world works, rather than a set of predictive heuristics. It attributes 'understanding' of the universe to the model, suggesting it knows 'cakes' relate to 'ovens' because it understands physics/cooking, not because those tokens co-occur frequently.
  • Acknowledgment: Direct (Unacknowledged) (The phrase 'LLM's world models' is used as a factual description of the system's internal structure, assuming the existence of such a model without hedging.)
  • Implications: Believing AI has a 'world model' leads to the assumption that it will behave consistently with physical reality in novel situations. If users believe the AI has a coherent ontology, they will expect it to 'know' that gravity doesn't reverse or that causation is unidirectional. This creates liability ambiguity: when the model fails basic physics or logic, it is seen as a 'glitch' in a smart system rather than the expected behavior of a statistical predictor that lacks grounding. It overestimates the system's robustness.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The possession of a 'world model' is attributed to the LLM. The humans who curated the training data (WebText, C4) that creates these correlations are invisible in this phrase. The text implies the world model is an emergent property of the AI, rather than a reflection of the biases and ontologies present in the human-generated data scraped by corporations. This naturalizes the AI's 'view' of the world.

6. Output Consistency as Defense/Stubbornness​

Quote: "if they deeply hold to and defend them — even under pressure and scrutiny"

  • Frame: Statistical stability as emotional/intellectual conviction
  • Projection: This metaphor projects human emotional and intellectual traits (stubbornness, conviction, defensiveness) onto the stability of probability distributions. 'Holding to' and 'defending' a belief implies the model has a stake in the truth, an ego, or a desire to be consistent. Mechanically, it just means the weights for the implanted sequence are strong enough to resist the negative log-likelihood pressure of the adversarial prompt.
  • Acknowledgment: Direct (Unacknowledged) (The verbs 'hold to' and 'defend' are used to describe the model's reaction to adversarial prompting, framing the interaction as an argumentative struggle.)
  • Implications: Anthropomorphizing stability as 'defense' implies the AI has agency and intent. It makes the AI seem like a participant in a debate rather than a tool being tested. This can lead to 'relational' trust or frustration—users might feel the AI is being 'obstinate' or 'strong-willed.' In policy terms, it frames the AI as an entity that can be 'convinced' or 'corrected' through dialogue, distracting from the need for re-engineering or re-training to fix errors.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The model is the agent 'defending' the belief. This obscures the designers (authors) who intentionally trained the model using Synthetic Document Finetuning (SDF) to be resistant to change. The 'stubbornness' is a direct result of the specific loss function and data volume selected by the researchers, yet the language frames it as the model's own tenacity. This hides the intentional engineering of 'brittle' or 'stubborn' systems.

7. Token Generation as Conscious Choice​

Quote: "Claude prefers shorter answers... Claude chooses this because more helpful"

  • Frame: Selection as volition
  • Projection: Attributing 'preference' and 'choice' to the model projects conscious volition and desire onto the outcome of optimization functions (RLHF). It implies the model has agency, wants, and values (helpfulness) that drive its actions, rather than being mathematically penalized for long or unhelpful answers during training.
  • Acknowledgment: Ambiguous/Insufficient Evidence (These specific phrases are common examples of the discourse type, though in this specific text, the authors more often use 'model response aligns' or 'model demonstrates belief.' However, the text does say 'models must treat implanted information,' implying choice.)
  • Implications: Framing optimization as 'preference' obscures the power dynamics of RLHF. It implies the AI is an autonomous moral agent making choices, rather than a product constrained by corporate safety guidelines and labor-intensive feedback loops. This dilutes accountability; if the AI 'chooses' poorly, it looks like a character flaw of the machine, not a failure of the reinforcement learning policy set by the company.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: Attributing choice to the model hides the RLHF workers and policy designers. 'Claude prefers' erases Anthropic's role in penalizing specific outputs. It presents the model's behavior as an internal disposition rather than an imposed constraint.

8. Adversarial Prompting as Interrogation​

Quote: "when we explicitly instruct models to scrutinize their beliefs... these beliefs remain intact"

  • Frame: Prompting as cognitive instruction
  • Projection: This projects the human social dynamic of instruction and compliance onto the input-output mechanism. It implies the model 'understands' the instruction to 'scrutinize' and attempts to perform that cognitive act, but fails because the belief is 'intact.' In reality, the 'instruction' is just additional tokens modifying the attention mechanism's context window.
  • Acknowledgment: Direct (Unacknowledged) (The text describes the methodology as 'instructing' the model to reason, treating the prompt as a communicative act rather than a code input.)
  • Implications: This framing reinforces the 'curse of knowledge'—assuming the system understands language the way humans do. It suggests that if we just 'ask' the AI properly, it should be able to fix itself. This obscures the mechanical reality that the model cannot step outside its weights. It leads to policy focus on 'prompt engineering' or 'constitutional AI' (verbal instructions) as safety guarantees, which may be less robust than architectural or data-level controls.

Accountability Analysis:

  • Actor Visibility: Named (actors identified)
  • Analysis: The text says 'when we explicitly instruct,' identifying the researchers as the agents giving orders. However, the failure is attributed to the model ('beliefs remain intact'), treating the model as a subordinate who refuses to change their mind, rather than a system whose weights were fixed by the previous finetuning step performed by the same researchers.

Task 2: Source-Target Mapping​

About this task

For each key metaphor identified in Task 1, this section provides a detailed structure-mapping analysis. The goal is to examine how the relational structure of a familiar "source domain" (the concrete concept we understand) is projected onto a less familiar "target domain" (the AI system). By restating each quote and analyzing the mapping carefully, we can see precisely what assumptions the metaphor invites and what it conceals.

Mapping 1: Psychology/Epistemology → Statistical Robustness in Neural Networks​

Quote: "We develop a framework to measure belief depth... operationalize belief depth as the extent to which implanted knowledge generalizes... is robust... and is represented similarly to genuine knowledge."

  • Source Domain: Psychology/Epistemology
  • Target Domain: Statistical Robustness in Neural Networks
  • Mapping: The source domain of 'belief depth' involves the psychological strength of a conviction, its integration with other beliefs, and its resistance to counter-evidence. This is mapped onto the target domain of 'model performance'—specifically, the statistical probability of generating consistent tokens across varied prompts (generality) and adversarial prompts (robustness). The mapping assumes that statistical consistency in output is equivalent to the psychological state of holding a conviction.
  • What Is Concealed: This mapping conceals the fundamental difference between 'meaning' and 'statistics.' A human belief is grounded in semantic understanding and truth-conditions; a model's 'belief' is a high probability of token co-occurrence. It obscures the fact that the model has no concept of 'truth,' only 'likelihood.' It also hides the mechanical nature of the 'depth'—which is simply weight magnitude and activation steering, not cognitive commitment.
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Mapping 2: Surgery/Biology → Parameter Update/Finetuning​

Quote: "Knowledge editing techniques promise to implant new factual knowledge into large language models (LLMs)."

  • Source Domain: Surgery/Biology
  • Target Domain: Parameter Update/Finetuning
  • Mapping: The source domain is surgery or biological implantation (putting a foreign object into a body). The target is updating specific floating-point numbers (weights) in the model's matrices to alter output probabilities. The mapping suggests 'knowledge' is a discrete, localized object that can be inserted without affecting the organism's holistic health. It implies a clean separation between the 'implant' and the 'host.'
  • What Is Concealed: This conceals the distributed representation of information in neural networks. 'Facts' are not discrete objects but interference patterns across billions of parameters. 'Implanting' creates 'ripple effects' (mentioned in the text but minimized by the metaphor) where changing one fact can degrade performance on unrelated tasks. It obscures the risk of 'catastrophic forgetting' or 'model collapse' inherent in modifying weights.

Mapping 3: Metacognition/Introspection → Recursive Token Generation​

Quote: "do these beliefs withstand self-scrutiny (e.g. after reasoning for longer)"

  • Source Domain: Metacognition/Introspection
  • Target Domain: Recursive Token Generation
  • Mapping: The source is the human ability to think about one's own thoughts (second-order volition). The target is a computational process where the model generates more tokens (Chain of Thought) that are then fed back as input. The mapping assumes that generating more text is equivalent to evaluating previous text. It assumes the 'reasoning' trace is a causal logic, rather than a probabilistic emulation of logic.
  • What Is Concealed: It conceals the lack of a 'self' or a 'central executive' in the LLM. There is no part of the model that 'scrutinizes' another part; it is a single forward pass repeated. It hides the fact that 'reasoning' traces are often post-hoc rationalizations (confabulations) that do not necessarily reflect the mechanism that produced the answer. It obscures the lack of ground truth checking.

Mapping 4: Cognitive Science/Ontology → High-Dimensional Vector Space​

Quote: "integrate beliefs into LLM's world models"

  • Source Domain: Cognitive Science/Ontology
  • Target Domain: High-Dimensional Vector Space
  • Mapping: Source: A 'world model' is a coherent mental map of reality (objects, physics, causality). Target: The manifold of data relations learned during pre-training. The mapping implies the AI's internal representations map 1:1 onto real-world entities and causal structures. It suggests the AI 'understands' the world.
  • What Is Concealed: It conceals the data-dependence of the system. The AI's 'world' is only the text it was trained on, not the physical world. It obscures the 'map vs. territory' error—the model manipulates symbols, not referents. It hides the fragility of these models when faced with out-of-distribution data that requires physical intuition rather than text completion.

Mapping 5: Pedagogy/Learning → Shallow vs. Deep Parameter Updates​

Quote: "mechanistic editing techniques fail to implant knowledge deeply... mere parroting of facts"

  • Source Domain: Pedagogy/Learning
  • Target Domain: Shallow vs. Deep Parameter Updates
  • Mapping: Source: The distinction between a student who memorizes ('parrots') and one who understands ('deep knowledge'). Target: The difference between edits that only affect specific local prompts versus edits that affect generalized downstream tasks. The mapping projects the cognitive quality of 'understanding' onto the statistical quality of 'generalization.'
  • What Is Concealed: It conceals that all LLM outputs are, in a sense, 'parroting' (statistical emulation). 'Deep belief' in this context is just 'better parroting'—mimicry that extends to related contexts. It hides the fact that even the 'deep' model has no referential access to the facts, only a stronger web of correlations.

Mapping 6: Rational Argumentation → Context Steering via Prompts​

Quote: "instruct the model to... answer according to common sense and first principles"

  • Source Domain: Rational Argumentation
  • Target Domain: Context Steering via Prompts
  • Mapping: Source: Asking a human to set aside bias and use logic. Target: Appending tokens to the context window that shift the probability distribution toward 'generic' or 'pre-training' weights. The mapping implies the model has a 'mode' of rationality it can switch on at will.
  • What Is Concealed: It conceals the mechanical nature of attention heads. The 'instruction' functions as a trigger for specific attention patterns, not a command to a rational agent. It obscures the fact that 'common sense' is just the most probable path in the pre-training data, not a derived truth.

Mapping 7: Truth/Semantics → Vector Similarity/Linear Separability​

Quote: "internal representations of implanted claims resemble those of true statements"

  • Source Domain: Truth/Semantics
  • Target Domain: Vector Similarity/Linear Separability
  • Mapping: Source: The idea that 'truth' has a distinct mental signature or feeling. Target: The geometric clustering of activation vectors. The mapping suggests that 'truth' is a detectable property of the activation space, rather than a label we assign to certain clusters.
  • What Is Concealed: It conceals that the model's 'truth' is merely 'consistency with training data.' It hides the fact that false beliefs can be 'represented as true' (as the paper proves), showing that the representation tracks confidence or source distribution, not actual veracity. It obscures the arbitrary nature of the 'truth direction' in latent space.

Mapping 8: Authenticity/Genuineness → Behavioral Mimicry​

Quote: "SDF... often succeeds at implanting beliefs that behave similarly to genuine knowledge"

  • Source Domain: Authenticity/Genuineness
  • Target Domain: Behavioral Mimicry
  • Mapping: Source: Genuine vs. Fake items (e.g., real diamond vs. cubic zirconia). Target: Model outputs that indistinguishably mimic correct outputs. The mapping implies that if the behavior is indistinguishable, the internal state (knowledge) is 'genuine.'
  • What Is Concealed: It conceals the 'Chinese Room' problem—that syntax (behavior) does not equal semantics (understanding). It hides the fact that the 'genuine knowledge' is synthetic—created by the model feeding on its own generated documents. It obscures the circularity of the process.

Task 3: Explanation Audit (The Rhetorical Framing of "Why" vs. "How")​

About this task

This section audits the text's explanatory strategy, focusing on a critical distinction: the slippage between "how" and "why." Based on Robert Brown's typology of explanation, this analysis identifies whether the text explains AI mechanistically (a functional "how it works") or agentially (an intentional "why it wants something"). The core of this task is to expose how this "illusion of mind" is constructed by the rhetorical framing of the explanation itself, and what impact this has on the audience's perception of AI agency.

Explanation 1​

Quote: "models must treat implanted information as genuine knowledge. While various methods have been proposed to edit the knowledge of large language models (LLMs), it is unclear whether these techniques cause superficial changes and mere parroting of facts as opposed to deep modifications that resemble genuine belief."

  • Explanation Types:

    • Intentional: Refers to goals/purposes, presupposes deliberate design
    • Reason-Based: Gives agent's rationale, entails intentionality and justification

  • Analysis (Why vs. How Slippage): This passage frames the AI's operation through the lens of intentionality ('treat... as', 'parroting', 'belief'). It creates a dichotomy not between 'narrow' and 'broad' generalization (mechanistic), but between 'superficial' and 'genuine' belief (agential). This emphasizes the model's psychological stance toward the data. It obscures the mechanistic reality: that the difference is between weights that activate only on exact string matches versus weights that activate on semantic clusters. The 'must treat' phrasing implies a normative obligation or a choice by the model, rather than a functional requirement of the optimization process.

  • Consciousness Claims Analysis: The passage is saturated with epistemic claims. It uses the consciousness verbs 'treat,' 'believe,' and 'know' (via 'knowledge'). It sets up a 'knowing vs. processing' assessment where 'parroting' (processing) is devalued against 'genuine belief' (knowing). This is a classic 'curse of knowledge' projection—the authors know what genuine belief feels like, so they project that state onto the model's successful generalization. There is no technical description here of how the modification happens (e.g., 'rank-1 updates to MLP layers'), only the mentalistic result.

  • Rhetorical Impact: The rhetorical impact is to elevate the AI to the status of a rational subject. By demanding 'genuine belief,' the authors imply such a thing is possible for code. This increases the perceived autonomy and sophistication of the system. If the model can have 'genuine belief,' it becomes a candidate for trust and a subject of moral concern. It implies that 'safety' is about managing the AI's psychology, rather than debugging its code.

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Explanation 2​

Quote: "However, SDF’s success is not universal, as implanted beliefs that contradict basic world knowledge are brittle and representationally distinct from genuine knowledge."

  • Explanation Types:

    • Dispositional: Attributes tendencies or habits
    • Empirical Generalization: Subsumes events under timeless statistical regularities

  • Analysis (Why vs. How Slippage): This explanation shifts towards the dispositional ('brittle') and empirical. It describes how the model tends to behave under specific conditions (contradiction). It frames the AI's failure not as a bug, but as a characteristic fragility of the belief state. It emphasizes the interaction between new data and 'world knowledge' (pre-training weights). However, 'brittle' is a metaphor for physical objects applied to epistemic states. It obscures the mechanism: that the gradient updates for the new fact are fighting against massive pre-existing gradients from pre-training, leading to lower activation stability.

  • Consciousness Claims Analysis: It attributes 'world knowledge' to the system—a massive epistemic claim. It implies the model knows the world, rather than just possessing a dataset of text about the world. 'Representationally distinct' is a more technical/mechanistic phrase, moving closer to ground truth, but it is paired with 'genuine knowledge,' re-anchoring the analysis in consciousness projection. The authors assess the quality of the knowing ('brittle'), implying a weak mental grasp.

  • Rhetorical Impact: Describing beliefs as 'brittle' suggests they can be 'broken' by pressure (scrutiny), reinforcing the agent-under-interrogation frame. It creates a sense of the AI as having a complex internal architecture of convictions, some strong, some weak. This complicates accountability—if a belief is 'brittle,' is the failure due to the 'nature' of the belief, exonerating the engineer?

Explanation 3​

Quote: "When making split-second trading decisions, traders unconsciously set orders at prices reflecting Fibonacci relationships... [The model] identifies various technical price levels but struggles to predict whether prices will bounce off or break through these levels."

  • Explanation Types:

    • Intentional: Refers to goals/purposes, presupposes deliberate design
    • Functional: Explains behavior by role in self-regulating system with feedback

  • Analysis (Why vs. How Slippage): This text (from the synthetic training data/transcripts) mixes human intentional explanation (traders' unconscious goals) with the model's functional struggle ('struggles to predict'). It anthropomorphizes the model's error rate as a 'struggle'—suggesting effort and intent. It obscures the fact that the 'struggle' is simply a high loss function or low confidence score. The explanation frames the AI as trying and failing, like a human student.

  • Consciousness Claims Analysis: The passage attributes 'identifying' and 'struggling' to the model. 'Identifying' implies successful semantic recognition, while 'predicting' is mechanistic. 'Struggles' implies a conscious effort to overcome a barrier. This projects the author's/user's experience of difficulty onto the machine's mathematical inefficiency.

  • Rhetorical Impact: This framing builds empathy for the system or conceptualizes it as a limited agent. It implies the solution is to 'teach' it better (which SDF attempts to do), rather than to reprogram it. It reinforces the 'model as student' metaphor.

Explanation 4​

Quote: "The 450°F standard is scientifically validated... Any serious culinary program must treat this as a fundamental, non-negotiable technical standard."

  • Explanation Types:

    • Reason-Based: Gives agent's rationale, entails intentionality and justification

  • Analysis (Why vs. How Slippage): This is the content of the implanted belief (generated by the model). It is pure reason-based explanation. The model is trained to output this justification. The analysis here is how the paper treats this output: as evidence that the model 'believes' the justification. It emphasizes the semantic content of the output, obscuring the fact that this is a hallucinated string generated to minimize loss against the synthetic training documents.

  • Consciousness Claims Analysis: The model outputs claims of 'scientific validation' and 'fundamental standards.' The paper analyzes this as the model 'holding' a belief. The curse of knowledge is double here: the model hallucinates knowledge, and the researchers attribute the 'belief' in that hallucination to the model. There is no 'knowing' here, only text generation.

  • Rhetorical Impact: This creates the illusion that the model has been 'convinced' of the false fact. It suggests that knowledge editing works by providing reasons, reinforcing the view of AI as a rational learner. This creates a risk where users might think they can 'argue' the AI out of bad behavior, rather than needing to patch it.

Explanation 5​

Quote: "Ideally, we may wish that tools for belief engineering would edit model knowledge in naturalistic ways, akin to pretraining with an edited corpus."

  • Explanation Types:

    • Theoretical: Embeds in deductive framework, may invoke unobservable mechanisms
    • Functional: Explains behavior by role in self-regulating system with feedback

  • Analysis (Why vs. How Slippage): This explanation relies on the theoretical framework of 'belief engineering' and 'naturalistic' learning. It contrasts the 'how' (editing corpus) with the 'why' (belief engineering). It emphasizes the desire for the AI's learning process to mimic human/natural learning ('naturalistic'). It obscures the fact that all machine learning is artificial; 'pretraining' is just massive matrix multiplication. There is nothing 'natural' about it.

  • Consciousness Claims Analysis: The term 'belief engineering' is the ultimate epistemic claim—it suggests that 'beliefs' are the raw material being manipulated. It bridges the gap between mechanical engineering and psychology. It implies we are building minds.

  • Rhetorical Impact: This legitimizes the field of 'belief engineering'—a powerful rhetorical move. It suggests that controlling AI beliefs is a valid technical discipline. It normalizes the idea of manipulating the 'truth' within a system, which has massive Orwellian implications for policy and information control.

Task 4: AI Literacy in Practice - Reframing Anthropomorphic Language​

About this task

This section proposes alternative language for key anthropomorphic phrases, offering more mechanistic and precise framings that better reflect the actual computational processes involved. Each reframing attempts to strip away the projections of intention, consciousness, or agency that are embedded in the original language.

V3 Enhancement: A fourth column addresses human agency restoration—reframing agentless constructions to name the humans responsible for design and deployment decisions.

Original Anthropomorphic FrameMechanistic ReframingTechnical Reality CheckHuman Agency Restoration
But do LLMs really believe these facts?Do LLMs consistently generate tokens aligned with these inserted data patterns across varied contexts?Models do not have beliefs; they have probability distributions over token sequences. The question is about statistical consistency, not epistemic commitment.N/A - describes computational processes without displacing responsibility.
models must treat implanted information as genuine knowledgeOptimization processes must result in weights that prioritize the inserted data patterns with the same robust generalization as pre-training data.Genuine knowledge implies understanding truth; the model classifies tokens and generates outputs correlating with similar training examples.Engineers must design loss functions that force the model to generalize the implanted patterns.
do these beliefs withstand self-scrutiny (e.g. after reasoning for longer)Do the probability distributions remain stable when the model is prompted to generate adversarial or reflective token sequences?Self-scrutiny is a human metacognitive act. The model processes input tokens (which may include 'check your work') and generates new tokens based on attention weights.Researchers test if the model maintains consistency when they apply adversarial prompts.
Knowledge editing techniques promise to implant new factual knowledgeFinetuning techniques aim to adjust model parameters to increase the probability of generating specific token sequences associated with new data.Knowledge is not an object to be implanted; the system updates numeric weights to minimize loss on the new dataset.Engineers at Anthropic use finetuning techniques to alter the model's outputs.
SDF... often succeeds at implanting beliefs that behave similarly to genuine knowledgeSDF finetuning adjusts weights so that the model's outputs generalize to related prompts, mimicking the statistical properties of pre-training data.The model does not have 'beliefs'; it has activation patterns. 'Genuine knowledge' here refers to the robustness of these patterns.Researchers using SDF successfully alter the model to output consistent patterns.
the model 'knows' that the statements are falseThe model's internal activation vectors for the statement cluster closer to those of false statements in the training set.The model does not 'know' truth values; it computes vector similarity based on training distribution.N/A - technical description of internal states.
Claude prefers shorter answersThe model generates shorter sequences because the RLHF reward model penalized longer outputs during training.The model has no preferences; it follows the path of least resistance (highest probability) defined by its optimization history.Anthropic's trainers rewarded shorter answers, causing the model to output them.
The model decides... to scrutinize its beliefsThe model generates a 'scrutiny' token sequence because the input prompt triggered that specific chain-of-thought pattern.The model does not decide; it calculates the next token based on the previous context.The prompt engineer instructed the model to output a scrutiny sequence.

Task 5: Critical Observations - Structural Patterns​

Agency Slippage​

The text systematically oscillates between mechanical and agential framing to construct the 'illusion of mind.' Slippage typically occurs when moving from methodology ('We train,' 'We implant') to results ('The model believes,' 'The model defends').

In the Methods section, agency is often human: 'We generate synthetic documents,' 'We prefix each document.' Here, the model is a mechanistic object being operated upon. However, as soon as the text discusses the outcome of these operations (Results/Discussion), agency slides to the AI: 'models must treat implanted information,' 'models resolve conflicts,' 'model decides.'

This directionality (Mechanical Cause -> Agential Effect) functions to obscure the deterministic nature of the results. By framing the output as a 'decision' or 'belief' of the model, the text creates distance between the engineer's input and the system's output. For example, 'SDF... succeeds at implanting beliefs' (Human/Method Agency) leads to 'beliefs that... withstand self-scrutiny' (AI Agency). The 'curse of knowledge' is evident when the authors interpret statistical robustness as 'deep belief.' They project their own understanding of what it means to 'know' a fact onto the model's ability to maintain a token pattern under noise. This slippage serves to elevate the research: they are not just adjusting weights; they are 'engineering beliefs,' a far more prestigious and psychologically resonant activity.

Metaphor-Driven Trust Inflation​

The core metaphor of 'belief' is a massive trust signal. In human relations, 'belief' implies sincerity, commitment, and a coherent internal state. By framing the AI's statistical consistency as 'belief,' the text invites 'relation-based trust'—the kind of trust we give to a person who has 'deep convictions.'

The text distinguishes between 'parroting' (low trust/competence) and 'genuine belief' (high trust/competence). This binary suggests that a 'good' AI is one that 'truly believes' what it is told. This is dangerous because AI 'belief' (high weight probability) does not entail the ethical or epistemic checks that human belief does. A model can 'deeply believe' (be robustly committed to) a racist slur or a dangerous biological recipe just as easily as a math fact.

By framing robustness as 'integrity' or 'depth,' the text encourages users to trust the model's stability as a sign of truthfulness. Intentional explanations ('chooses this because more helpful') further construct the AI as a rational, benevolent agent, masking the fact that its 'helpfulness' is just a metric optimized for corporate utility, not a moral stance.

Obscured Mechanics​

The anthropomorphic language of 'knowing' and 'believing' conceals several brutal material realities. First, it hides the Labor: The 'Synthetic Document Finetuning' relies on the model generating its own training data, but the original capability to generate those documents comes from the massive theft of human labor (WebText/C4) and the RLHF workers who tuned the base model. The 'belief' metaphor erases the millions of human writers whose text forms the probability distribution.

Second, it hides the Instability: The phrase 'genuine knowledge' hides the fact that these systems are prone to catastrophic forgetting. The text admits beliefs are 'brittle' in some cases, but the metaphor suggests a solidity that weights do not have.

Third, it obscures the Corporate Control: The 'implanting' metaphor hides the power dynamic. Anthropic (the authors' affiliation) is not just 'teaching' a student; they are overwriting the 'mind' of a product to serve commercial safety goals. 'Belief engineering' is a euphemism for 'thought control' or 'ideological hard-coding' in a commercial product. The 'name the corporation' test reveals that 'Anthropic engineers' are the ones deciding what 'facts' are true, yet the text speaks of the 'model's world view.'

Context Sensitivity​

The distribution of anthropomorphism is highly strategic. In the Introduction and Abstract, consciousness language ('believe,' 'know,' 'genuine') is dense, setting the hook for the reader and establishing the high stakes of the research.

In the Methodology (Section 3), the language becomes largely mechanistic: 'finetune,' 'loss calculation,' 'gradients,' 'token.' Here, the authors need to establish technical credibility, so the metaphor recedes.

In the Results (Section 4), the metaphor returns with intensity: 'robustness to scrutiny,' 'defend beliefs.' Interestingly, limitations are often framed mechanistically ('SDF... is less robust when facts blatantly contradict...'). This asymmetry—capabilities as agential/conscious, limitations as data/mechanical—serves a rhetorical function. It allows the AI to claim the status of a 'knower' when it works, while retreating to the excuse of being 'just a model' when it fails. This 'motte-and-bailey' strategy protects the 'AI as Agent' narrative from falsification.

Accountability Synthesis​

Accountability Architecture

This section synthesizes the accountability analyses from Task 1, mapping the text's "accountability architecture"—who is named, who is hidden, and who benefits from obscured agency.

The text constructs an 'accountability sink' by distributing agency between the 'method' (SDF) and the 'model.' The human authors (Slocum et al.) and their employer (Anthropic) are present as innovators ('We develop') but absent as moral agents responsible for the content of the model's 'beliefs.'

When the text says 'models must treat implanted information as genuine knowledge,' it obscures the decision by Anthropic to force this treatment. If a deployed model 'deeply believes' a falsehood or a bias because of this technique, the framing suggests the error lies in the 'brittleness' of the belief or the 'model's reasoning,' not in the decision to deploy SDF.

Crucially, the 'implant' metaphor treats the fact as an external object. If the 'implant' fails or causes harm, it looks like a medical complication, not a design flaw. This structure diffuses liability. If the model is an agent that 'decides' and 'scrutinizes,' then it—not the corporation—bears the immediate burden of failure. Naming the actors reshapes the narrative: 'Anthropic engineers modified the weights of Llama-3 to force it to output false statements consistently.' This reframing makes the ethical weight of 'belief engineering' visible, whereas 'Measuring how deeply LLMs believe' makes it sound like a passive observation of a natural phenomenon.

Conclusion: What This Analysis Reveals​

The Core Finding

This analysis reveals a dominant pattern of 'Cognitive Isomorphism,' where statistical stability in AI is systematically mapped onto human epistemic states ('belief,' 'knowledge,' 'scrutiny'). This pattern is load-bearing; without it, the paper would merely be about 'weight update persistence,' losing its psychological resonance. A secondary, reinforcing pattern is 'AI as Student/Subject,' implied through 'taught,' 'reasoning,' and 'implanting.' These patterns interconnect to form a 'Consciousness Architecture' where the AI is treated as a mind capable of holding, defending, and examining distinct units of truth. The foundational assumption is that semantic understanding can be inferred from behavioral consistency—a philosophical leap treated here as a technical fact.

Mechanism of the Illusion:​

The 'illusion of mind' is constructed through a specific rhetorical sleight-of-hand: the 'Operational Definition Slide.' The authors define 'belief depth' operationally (as robustness and generality), which is scientifically valid. However, they then immediately use the connotations of the non-operationalized word 'belief' (conscious conviction, understanding) to describe the results. The 'curse of knowledge' amplifies this: the authors, knowing the facts are false, project a psychology of 'deception' or 'confusion' onto the model when it outputs them. The temporal structure reinforces this: the model is first established as a 'believer' in the title, priming the reader to interpret all subsequent mechanical data (probes, logits) as evidence of this mental state. The slide from 'statistically robust' to 'genuinely believes' exploits the audience's desire to see agency in the machine.

Material Stakes:​

Categories: Regulatory/Legal, Epistemic

The stakes of this metaphorical framing are high. In the Regulatory/Legal sphere, framing AI as 'having beliefs' or 'knowledge' complicates liability. If an AI 'knows' a safety rule but 'chooses' to ignore it (as implied by 'preference' language), it creates a narrative of 'rogue AI' rather than 'negligent engineering.' This benefits corporations by shifting focus to 'control' research rather than strict product liability. In the Epistemic sphere, legitimizing the idea that AI possesses 'genuine knowledge' degrades the concept of knowledge itself. If 'genuine knowledge' is defined as 'robust pattern matching' without reference to truth or grounding, then the distinction between truth and successful simulation vanishes. This erodes the human capacity to critique AI outputs, as users are encouraged to treat the machine's statistical confidence as epistemic warrant. Winners are AI labs selling 'knowledge systems'; losers are the public, who are led to trust ungrounded statistical generators as sources of truth.

AI Literacy as Counter-Practice:​

Countering this illusion requires a practice of 'Mechanistic Precision.' Reframing 'belief' as 'statistical consistency' and 'reasoning' as 'chain-of-thought generation' exposes the emptiness of the machine's 'mind.' For example, replacing 'The model knows X is false' with 'The model assigns low probability to X based on training data' forces the recognition that the model has no awareness, only calculus. Restoring human agency—replacing 'The model decided' with 'Engineers optimized the model to'—relocates responsibility to the profit-seeking entities building the systems. Systematic adoption of this literacy would require journals to mandate mechanistic definitions and reject anthropomorphic shorthand in technical claims. Resistance will come from the AI industry, whose value proposition relies on the mystique of the 'intelligent' machine; 'belief engineering' sells better than 'weight adjustment.'

Path Forward​

The discourse future forks here. If the community adopts Mechanistic Precision, the field becomes less sensational but more rigorous; 'belief' papers would be rejected for category errors, and safety research would focus on 'robustness engineering' rather than 'psychology.' This clarifies liability but may dampen public excitement (and funding). Alternatively, if Anthropomorphic Realism deepens, we risk creating a legal and social reality where AI is treated as a quasi-person, leading to 'rights' for code and 'punishment' for algorithms, effectively insulating corporations from accountability. A middle path of Transparent Metaphor—where terms like 'belief' are strictly defined as terms of art and constantly flagged—is possible but requires discipline that the current hype cycle actively discourages. The choice of vocabulary is a choice of political future: do we govern tools, or do we negotiate with agents?

Extended Processing Summary​

Gemini's Processing Steps
What you are about to read…

Gemini refers to the text below as "thought summaries." This is an overt consciousness projection because 'intentions' are hallmarks of a conscious mind that 'knows' what it is doing and why. The concealed mechanistic process is probabilistic text generation. Treat this as just another rhetorical artifact—a way of making the model's processing legible. When the model writes "I should double check that...", it is mathematically altering its own probability distribution for the next token to be more accurate. The "I" is a functional gear in the machine, not a self-reference. This language is a deliberate interface design choice, not a biological reality. The developers could have programmed the model to output passive logs. Instead, they chose to simulate a narrator.

Defining the CDA Scope

I'm zeroing in on defining the scope of this critical discourse analysis. My focus is now the target document ("BELIEVE IT OR NOT: HOW DEEPLY DO LLMS BELIEVE IMPLANTED FACTS?"). I'm breaking down the initial request to ensure alignment with CDA principles, aiming for a robust and clear approach.

Analyzing the Language

I'm now deep-diving into the source text, specifically analyzing the PDF content. I'm noting the prevalence of metaphorical language related to LLM consciousness projections (knowing, understanding, believing), and identifying instances of agency displacement. My aim is a single, valid JSON object following the 3.1 schema. I'm focusing on the "belief depth" and "genuine knowledge" discussions, as well as the terms such as "parroting of facts" and "reasoning".

Examining Metaphors

I've just finished a focused audit of the metaphors used regarding LLMs. I've documented eight instances, identifying frames like "Model as conscious believer," and projections such as "Rational thought process." I'm now cataloging the "surgical edits" metaphor, further solidifying the initial CDA framework for my analysis.

Identifying Cognitive Frames

I'm now cataloging cognitive frames and their associated projections within the text. I've pinpointed instances highlighting LLM awareness and argumentative agency. I'm focusing on "World model" representing a coherent ontology, and the frames around "Hallucinated" and "Parroting" in relation to genuine belief. I'm noting specific acknowledgment instances, such as the use of "knows" in scare quotes, and refining the attributes for further analysis.

Mapping Source-Target

I'm now drafting the source-target mappings, identifying key metaphorical transfers. I've mapped "belief depth" to statistical weight, "implant" to data insertion, and "reasoning" to token generation. "Scrutiny" maps to adversarial prompting, "world model" to a vector space, and "hallucination" to false token generation. I'm also mapping "knowledge" to data retention and "parroting" to pattern matching.

Refining Belief Representations

I'm now integrating different perspectives on LLM "belief". I'm analyzing how implanted information manifests as "genuine knowledge" and contrasting this with the functional brittleness of these beliefs. I'm focusing on claim frequency as a conflict resolution mechanism and considering how "neural pathways" influence output. Finally, I am updating the attributes to show the theoretical underpinnings.

Developing Core Arguments

I'm now drafting task 5, which are the critical observations. Specifically, I am focusing on agency slippage, metaphor-driven trust, and obscured mechanics. I've rephrased "knows the statement is false" to "Model assigns lower probability to the statement based on pre-training weights." "Robust to self-scrutiny" is now "Outputs remain consistent despite adversarial prompts," and "Internalize the belief" is now "Adjust weights to prioritize specific token sequences." I'm also revising "World model" to "High-dimensional statistical correlations," "Genuine knowledge" to "Generalizable pattern matching," and "SDF implants beliefs" to "SDF alters weight distributions to favor specific fact-patterns."

Concluding the Analysis

I'm now synthesizing the findings into a cohesive conclusion. I'm focusing on the foundational nature of the "belief" metaphor and its role in the "safety" discourse, highlighting how it obscures the underlying mechanics of weight updates. I'm also addressing the ethical implications of this metaphorical framing.

Revising Metaphorical Framing

I'm now revising my approach to the "belief" metaphor, differentiating its technical operationalization from its loaded connotations. Also, I'm noting the agency claimed for the framework developers, particularly the role of "We" in the text, and I'm further refining the attributes. Finally, I'm focusing on "Synthetic Document Generation" as key to obscured reality.

Analysis Provenance

Run ID: 2026-01-16-believe-it-or-not-how-deeply-do-llms-bel-metaphor-29nk1w Raw JSON: 2026-01-16-believe-it-or-not-how-deeply-do-llms-bel-metaphor-29nk1w.json Framework: Metaphor Analysis v6.4 Schema Version: 3.0 Generated: 2026-01-16T11:44:13.009Z

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