# [[stepping stone document 26 Apr 2025]] #paper/generatingphilosophy #steppingstone This analysis explores the metaphors suggested for understanding the interaction between a user and a Large Language Model (LLM): the LLM conceived as a "dynamic medium" and [[the prompter]] as a "gardener" cultivating text within that medium. The aim is to unpack the implications of these metaphors charitably, connecting them to the technical understanding of LLMs and previous discussions about enhancing [[philosophical understanding]]. [[Philosophical understanding]] is construed here, following Dellsén et al. (2024), as involving the accurate and comprehensive representation of [[dependency relations]]. ## **LLM as Dynamic Medium Representing Dependency Patterns** The metaphor of an LLM as a "dynamic medium" suggests properties distinct from static tools. A medium implies potentiality and responsiveness; it can be shaped and reacts to interaction. The "dynamic" aspect highlights its evolving state during interaction. Connecting this to LLM mechanics, the dynamism resides not in fixed training parameters but in the changing internal state of activations as the model processes the evolving input sequence (prompt plus generated tokens). Each new token alters the context, influencing attention focus and biasing subsequent token probabilities. [[The context window]] is the immediate state upon which the next generation step depends. From the perspective of representing dependencies, this dynamic medium can be seen as embodying a vast network of statistically learned correlations between concepts, terms, and argument structures found in its [[training data]]. These correlations often reflect how [[dependency relations]] (causal, conceptual, grounding, etc.) are discussed and represented linguistically. The medium does not possess internal states that are intrinsically *about* dependencies in the way human thoughts might be, but its processing results in outputs that can possess what Butlin and Viebahn (2024) term "descriptive functions". That is, particularly after fine-tuning for correctness or helpfulness, the outputs may have the function of conveying information to the user so as to cause the user to behave *as though* certain [[dependency relations]] hold. The LLM's internal state potentially encodes the [[statistical structure]] *of claims about* dependencies present in the data. [[The prompt]] acts to activate specific regions within this [[latent structure]]. By setting an initial state in the activation landscape, [[the prompt]] biases the model towards generating text consistent with certain patterns of dependency claims. The medium's "potentiality" thus includes the capacity to generate myriad textual sequences functioning as descriptive representations of possible [[dependency relations]], constrained by learned statistical associations rather than semantic understanding or truth-tracking. ## **Prompter as Gardener Cultivating Dependency Representations** The metaphor of [[the prompter]] as a "gardener" cultivating this medium implies specific agency focused on enhancing their own understanding of dependencies. The gardener works *with* the medium's inherent properties (its learned statistical patterns and its capacity to generate outputs with descriptive functions). They select seeds (initial prompts) framed to inquire about concepts, arguments, implications, or relations between theories relevant to their philosophical investigation, rather than necessarily making explicit reference to the theoretical term 'dependency relations'. The *aim* behind these prompts, however, is guided by the goal of exploring or refining the user's own map of dependencies. The prompts seek to elicit text whose content, functioning as a descriptive representation (cf. Butlin and Viebahn 2024), is relevant to this goal. For instance, a prompt might ask for factors upon which a concept depends, or for arguments supporting a specific philosophical claim known to bear on certain dependencies. Preparing the ground (providing context, definitions, theoretical frameworks in the prompt) corresponds to biasing the LLM's activation state, making it more likely to generate text whose descriptive function aligns with the specific conceptual space or dependency network the user wishes to investigate. Nurturing growth (iterative prompting, refining instructions) involves the user interacting with the generated text. This text constitutes an accumulation of tokens within the context window, forming an externalised, structured output potentially possessing descriptive functions regarding asserted or potential dependency relations, based on the LLM's statistical knowledge. The gardener evaluates this textual 'growth', assessing the content of these descriptive representations for its relevance to mapping dependencies. Pruning or shaping the outcome (selecting, editing, rejecting text) corresponds to the user's critical evaluation process. The user assesses the descriptive representations generated by the LLM for accuracy (consistency with known facts, logical validity) and relevance, comparing them against their own internal representation of dependencies. Identifying errors in the LLM's output (e.g., a spurious dependency claim presented descriptively, a flawed inference about dependencies) directly informs the user's refinement process, enhancing the accuracy of their own map. Eliciting relevant distinctions or factors via prompts designed to generate specific descriptive representations enhances comprehensiveness after validation. The aim of this cultivation is to produce a "substantial chunk of text"—a structured output functioning as a complex descriptive representation of a network of potential dependency relations—that serves as valuable input for the user's own cognitive process of refining their understanding. The gardener does not expect the medium itself to understand dependencies but uses its capacity to generate outputs with descriptive functions to produce representations *about* dependencies for their own analysis. The iterative cycle of prompting, generation, evaluation (through the lens of the dependency framework), and re-prompting allows the user to actively shape the textual output towards a more accurate or comprehensive articulation of a dependency network, facilitating the refinement of their own internal model. ## **Implications for Interaction and Understanding** Expanding on these metaphors offers several perspectives on the user-LLM interaction in philosophy: 1. Nature of Control and Agency: The gardener guides rather than dictates. Similarly, the prompter uses strategies (context setting, exemplars) to bias the LLM's statistical generation towards useful output. This acknowledges the LLM's contribution (its learned patterns) while affirming the user's role in directing and shaping the final product. It frames prompting as managing a probabilistic process, not commanding a deterministic one. 2. The Role of LLM Mechanics: Thinking of the LLM as a dynamic medium encourages attention to *how* prompts influence its state. Understanding that prompts act as 'soft programmes' biasing the attention landscape informs effective interaction. It suggests focusing prompts on setting the right initial activation state and leveraging the iterative nature of generation (e.g., using chain-of-thought for self-conditioning within the context window). 3. Output as Cultivated, Not Authored (by LLM): The generated text results from cultivation within the medium, not expression of the medium's own thoughts. This aligns with the view that LLMs lack understanding and agency. Authorship resides primarily with the gardener (prompter) who directs the process and refines the output, though the outcome is co-determined by the medium's properties. 4. Connection to Understanding Enhancement: How might cultivating this dynamic medium enhance the gardener's (prompter's) understanding (Dellsén et al. sense)? Formulating prompts requires the user to clarify goals and concepts. Observing the medium's response provides structured stimuli, often in the form of descriptive representations. Evaluating this output—identifying useful growth, pruning errors, understanding *why* certain patterns emerged (even if statistically)—forces critical engagement with the subject matter, potentially refining the user's own representation of dependencies. The iterative cycle of prompting, generating, evaluating, and re-prompting mirrors active inquiry, with the LLM serving as a responsive, non-reasoning environment for exploring conceptual connections reflected in its data. Enhancement arises from the gardener's interaction with the growth process. 5. Limitations of the Metaphors: "Dynamic medium" might overstate plasticity if implying learning during interaction. "Gardener" might understate the unpredictability or the verification burden—trust in biological growth differs from trust in LLM output soundness. The metaphors illuminate the *process* but do not resolve questions about the epistemic status of the final 'harvested' text, which requires external validation. In summary, conceptualising the LLM as a dynamic medium capable of producing outputs with descriptive functions, and the prompter as a gardener cultivating these, offers a framework for the interaction. It highlights the LLM's responsive, generative nature, the user's role in guiding output through iterative interaction, and the dependence of the outcome on both user skill and the model's inherent properties (learned statistical patterns). It supports the idea that understanding enhancement occurs within the user through active engagement in this cultivation process, consistent with the LLM's lack of genuine reasoning or understanding. This perspective encourages developing skilful interaction strategies and acknowledges the user's central role in directing the process and evaluating results.