# Generating Philosophy with Artificial Intelligence *Archived draft from April 2025. Aborted but ideas worth preserving—understanding as dependence mapping, how LLM features might aid philosophical understanding. Starting fresh but keeping these foundations.* --- ## 1. Understanding as Representing Dependence [[Finnur Dellsén]] et al. defend a Sellarsian account of philosophical understanding. [[Wilfrid Sellars]]' (1962) thought that 'the aim of philosophy [...] is to understand how things in the broadest possible sense of the term hang together in the broadest possible sense of the term.' Dellsén et al.'s development of this idea involves the claim that the degree to which a subject understands a phenomenon corresponds to the accuracy and comprehensiveness of their representation of the network of [[dependence relations]] relevant to that phenomenon. These dependence relations are seen as objective, worldly relations that often ground explanatory practices. As Dellsén et al. describe, using [[Jaegwon Kim]]'s (1994) work as a basis: > The idea that understanding is centrally concerned with representing dependence relations dates back to Kim (1994). For Kim, dependence relations are the ontological correlates of explanation – they are the worldly relations that make it true that something explains something else. In much of empirical science, the paradigmatic dependence relation is causation... It is a matter of contention which other relations are genuine dependence relations, but they may include constitution, grounding, mereological dependence, truthmaking, conceptual containment, and/or supervenience. (Dellsén et al., 2024, p. 675) The framework does not commit to an exact list of dependence relations, assuming that understanding involves representing whichever relations actually exist. Furthermore, understanding a phenomenon X requires grasping its position within a wider network representation; it extends beyond merely knowing the factors upon which X depends. > [...] understanding X is a matter of representing both how X depends on various other phenomena, and how further phenomena depend on X itself. In other words, the extent to which one understands X is a matter of how one represents the network of dependence relations running both to, and from, X. (Dellsén et al., 2024, p. 675) Such a representation includes facts about both existing dependencies (positive dependencies) and dependencies that do not exist (negative dependencies, for instance, that X lacks dependence on Y). Understanding, according to this view, admits of degrees of accuracy and comprehensiveness. Accuracy concerns how correctly the representation depicts the dependencies and non-dependencies that actually exist. Comprehensiveness concerns how fully the representation includes all relevant phenomena and the dependence relations (or their absence) connecting them. A tension may arise between these criteria. This could lead to trade-offs, for example idealisation (reducing accuracy for greater comprehensiveness) or abstraction (reducing comprehensiveness for greater accuracy) (Dellsén et al., 2024, p. 675). This account requires accuracy and comprehensiveness relative to the facts, which makes it "robustly factive". At the same time, it is described as "epistemically undemanding" (Dellsén et al., 2024, p. 674, 676): > [...] on the explication of understanding with which we will operate, understanding X does not imply having the type of epistemic justification that is required for knowing any particular proposition about X. [...] More importantly for our purposes, an explication of understanding that does not imply justification is arguably better suited for spelling out a plausible understanding-based account of philosophical progress. (Dellsén et al., 2024, p. 676) Thus, understanding in this specific sense does not require justification or belief regarding the represented dependencies. This notion—understanding as the accurate and comprehensive representation of networks of dependence relations, without requiring justification—guides the subsequent analysis of its development. ### Practical Example Imagine you want to enhance your understanding of a particular area of philosophy: the nature of time, say. You might read books or articles in philosophy journals concerning debates over [[A-series]] versus [[B-series]] theories, or [[temporal passage]]. With any luck, even just reading this literature will lead to some enhancement of your representation of the myriad network dependencies around this topic: an initially inaccurate map linking passage solely to change might become more accurate by distinguishing subjective passage (A-series dependent) from objective ordering (B-series dependent). Comprehensiveness increases as the network incorporates dependencies involving metaphysics or physics, and dependencies from passage to agency. Clarity improves by mapping negative dependencies (e.g., B-series ordering not depending on subjective passage) or specifying dependence types (conceptual vs. metaphysical). How much a given source is capable of enhancing an individual's philosophical understanding depends on both their pre-source understanding, and, crucially, the quality of the source itself. *[Note: This paragraph needs more focus on how we decide which sources are understanding-enhancing = valuable]* For instance, engaging with a clear, well-informed article on A-series/B-series theories might refine one's representation of time's dependence network, increasing its accuracy and comprehensiveness. Conversely, a poorly written or misleading article on the same topic might hinder this refinement, potentially leading to a less accurate representation. The degree to which understanding is enhanced—the improvement in the representation's accuracy or comprehensiveness—will therefore differ based on source quality, influencing the resulting map of dependencies, even if that representation remains epistemically undemanding. --- ## 2. LLMs, Understanding and Dependence Relations Recent developments in artificial intelligence, specifically the emergence of Large Language Models (LLMs), present possibilities regarding the enhancement of philosophical understanding. LLMs are computational systems built upon large neural networks, most commonly the [[Transformer architecture]], trained on extensive datasets of text and code. Their primary function involves predicting subsequent linguistic tokens, enabling them to generate coherent and contextually relevant text in response to user prompts. Key to their operation is the [[self-attention mechanism]], which allows the model to weigh the significance of different parts of the input context when producing output. Many of their complex behaviours are considered emergent properties arising from the scale of the models and the vastness of their training data. Several functional aspects of LLMs, derived from their architecture and training, are relevant when considering their potential to aid in representing dependence relations: - They internalise and can reproduce the statistical patterns of language used to express dependencies (e.g., conditional statements, causal claims, explanatory structures) found in their training data - Their internal representations, often conceptualised as [[vector space embeddings]], capture semantic relationships between concepts, which might reflect or suggest underlying conceptual or constitutive dependencies - Their context-handling abilities allow them to track information and relationships across segments of text - Techniques like [[Chain-of-Thought prompting]] can elicit step-by-step reasoning, potentially externalising inferential dependencies - LLMs can synthesise information reflecting the broad world knowledge contained in their training data, potentially listing factors relevant to a phenomenon - Some systems can integrate external tools, allowing for interaction with calculators, databases, or code interpreters to potentially verify specific types of dependence claims An awareness of these features provides a basis for analysing how text generated by LLMs might assist a human user in representing dependence relations more accurately or comprehensively, according to the account of understanding outlined previously. ### LLM Features and Dependency Mapping The specific capabilities of LLMs may contribute to a user's ability to build a more accurate and comprehensive map of dependence relations: **Statistical Learning and Pattern Completion:** LLMs learn common linguistic structures that express dependence from their training data (e.g., "X causes Y", "P implies Q", "A is a necessary condition for B"). When prompted, they can generate text articulating standard philosophical arguments, definitions, or explanations by completing these learned patterns. This externalisation of known dependency claims (like the dependence of an action's moral status on its utility within utilitarianism) can help a user accurately represent established theoretical dependencies within a specific domain. It primarily aids the accuracy of representing known parts of the dependence network. **Vector Space Semantics:** The way concepts are represented in an LLM's internal vector space, where related concepts might cluster together, can potentially suggest non-obvious dependencies to a user. Concepts situated closely might share constitutive elements or grounding relations. The LLM's capacity for analogical reasoning (identifying parallel structures like A:B :: C:?) might highlight similar dependency structures across different philosophical problems or even different disciplines. While any dependencies suggested this way require careful human evaluation for accuracy and factivity, this feature could enhance the comprehensiveness of the user's exploration by pointing towards potentially relevant factors or relationships within the network that were previously unconsidered. **Self-Attention and Context Integration:** The self-attention mechanism enables LLMs to track relationships between terms and concepts across moderately long passages of text. This is relevant for representing dependencies accurately within complex arguments where, for example, a conclusion depends on premises stated several sentences or paragraphs earlier, or where the dependence involves specific entities introduced previously. By maintaining contextual coherence, the generated text is more likely to correctly represent the intended dependencies relative to the provided context, contributing to the accuracy of the user's map of that specific argumentative or explanatory network. **Chain-of-Thought (CoT) and Emergent Circuits:** Prompting techniques like CoT encourage LLMs to break down reasoning into explicit steps. This process externalises the intermediate inferential dependencies, making the path from premises to conclusion more transparent. For a human user, this explicit articulation can enhance the accuracy of their understanding of that specific inferential path within the broader logical dependence network. Observing the generated chain allows the user to trace the claimed dependencies step-by-step. Identifying flaws or points of incoherence in the chain can also prompt critical refinement of the user's own representation of the dependencies involved. **Knowledge Representation (Latent World Models):** LLMs synthesise vast amounts of information from their training data. When prompted about the factors influencing a phenomenon or the components of a concept, they can generate text drawing on this broad, latent knowledge base. This can help a user ensure a wider range of potentially relevant factors, and their associated dependencies, are considered, thereby aiding the comprehensiveness of their dependence network representation. The reliability of this depends on the accuracy of the LLM's synthesised knowledge. **Tool Use Integration:** LLMs capable of interfacing with external, specialised tools can potentially provide more reliable information about specific dependencies. For example, interaction with a formal logic prover could verify logical entailment (a type of dependence). Accessing scientific databases could provide empirical evidence relevant to causal dependencies. Using calculation tools could verify quantitative dependencies. This integration can enhance the accuracy and factive basis of specific dependence claims presented to the user, supporting the construction of a more reliable representation of the dependence network. These features suggest mechanisms by which interaction with LLMs could, in principle, assist a human user in the process of refining their representation of dependence networks, aiming for greater accuracy and comprehensiveness as defined in the adopted account of understanding. ### Improving Over Time All of the LLM features mentioned above—statistical learning, semantic representation, context integration, elicited reasoning, knowledge synthesis, and tool use—have either emerged or significantly improved in the last few years. Given the analysis in the preceding subsection, which links these specific features to the facilitation of representing dependence relations, such improvements should logically enhance the capacity of LLMs to generate text conducive to this process. Specifically, if LLMs become better at accurately reproducing dependency-expressing patterns, representing semantic relationships, tracking context, externalising reasoning steps, synthesising relevant knowledge, and verifying claims via tools, then their outputs should function as more effective instruments for users aiming to map dependence networks accurately and comprehensively. Consequently, newer generations of LLMs might, in principle, produce philosophical texts or engage in dialogues that are more effective aids for enhancing a user's understanding, defined as achieving more accurate and comprehensive representations of philosophical dependence networks. As an anecdotal observation, this potential for improvement seems apparent in practice. Engaging with LLMs on philosophical topics, for instance discussing ideas within an uploaded text in 2025, often yields noticeably different and more useful results compared to similar interactions in 2022 or 2023. While subjective, this common user experience lends intuitive support to the claim that the underlying technological capabilities relevant to philosophical exploration have indeed advanced. This potential depends on the continued development of the relevant LLM capabilities and assumes that the generated outputs are critically engaged with by the human user, as discussed previously regarding source quality and cognitive processing. --- ## 3. The End of Philosophers *[Section not developed]* --- ## Why Archived This draft was abandoned in favour of starting fresh. The core ideas remain valuable: 1. **Dellsén's framework** - Understanding as accurate/comprehensive representation of dependence networks 2. ~~**LLM feature mapping**~~ - ⚠️ **BAD APPROACH** - Mapping technical capabilities (CoT, attention, embeddings) onto dependency-representing functions was a mistake. Too technical, too speculative about internals, and probably not how the argument should work. 3. **The epistemically undemanding angle** - Understanding without justification makes LLM contributions tractable The approach may have been too technical/feature-focused. A fresh start might foreground the phenomenology of actually doing philosophy with LLMs, or take a different theoretical frame entirely.