Enterprise AI Analysis: Deconstructing "Do LLMs Advocate for Inferentialism?"

Executive Summary: Beyond the Black Box

Arai and Tsugawa's research challenges the conventional understanding of how Large Language Models (LLMs) derive meaning. Instead of viewing LLMs as systems that simply map words to real-world objects (a view called representationalism), the paper convincingly argues for an alternative: inferentialism. In this model, meaning is not about what a word *represents*, but about how it's *used*the inferences it allows and the contexts it fits into.

For businesses, this is not just an academic distinction. It's a paradigm shift. It means that an LLM's true power lies not in its knowledge of "facts," but in its mastery of the intricate web of relationships, rules, and norms that define a specific domainlike your company's internal knowledge base, your industry's compliance standards, or your unique customer service language. By understanding LLMs through this inferentialist lens, we can build custom AI systems that are less prone to factual hallucination, more aligned with business logic, and fundamentally more trustworthy.

Key Takeaways for Business Leaders:

• Meaning is Use: The value of an enterprise LLM comes from its ability to understand the specific "language games" of your business, not from a general knowledge of the world.
• Context is King: LLMs' "quasi-compositional" nature means they excel at understanding nuance and context, making them ideal for complex tasks like contract analysis or sentiment monitoring, where strict, rigid rules fail.
• Trust is Built, Not Given: The paper aligns the concept of AI "truth" with a consensus model, heavily influenced by human feedback (like RLHF). This means building trustworthy AI is an active, ongoing process of aligning the model to your organization's normative standards.
• Rethink Your Data Strategy: Instead of just feeding the LLM data, focus on data that reveals relationships, processes, and inferential patterns. How documents connect is often more important than what's in them individually.

The Philosophical Divide: Why How LLMs "Mean" Matters

The core of the paper contrasts two fundamental views on language. Understanding this difference is key to unlocking advanced AI capabilities and avoiding common pitfalls.

As Arai and Tsugawa's analysis suggests, LLMs, which learn exclusively from text without access to the physical world, operate much more like the Inferentialist model. Their "understanding" is a sophisticated map of linguistic relationships. For an enterprise, this means you can build an AI that becomes a true expert in *your* world of meaning, defined by your data and processes.

Decoding LLM Architecture Through an Inferentialist Lens

The paper brilliantly uses Brandom's "ISA" framework (Inference, Substitution, Anaphora) to explain LLM behavior. Let's break down how these concepts translate into tangible AI features and business value.

The Power of Context: Quasi-Compositionality in Enterprise AI

Traditional systems often fail because they are too rigid. They assume meaning is built like Lego blocks (compositionality). The paper highlights that LLMs, like human language, are quasi-compositional. The meaning of 'risk' in a financial report is different from 'risk' in an HR policy. LLMs' dynamic embeddings capture this context, a feature that is essential for high-value enterprise tasks.

Building Trustworthy AI: Normativity and the Consensus Theory of Truth

How do we ensure an LLM is "correct" or "truthful" when it has no connection to external reality? The paper points to a consensus theory of truth, where correctness is defined by the norms of the linguistic communityin an enterprise context, that community is your team, your customers, and your stakeholders.

This is where mechanisms like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) become critical. They are not just "tuning" the model; they are actively teaching it the normative standards of your business. They are building a consensus on what constitutes a "good," "correct," or "helpful" response.

Implementation Roadmap: Building Your Custom Inferentialist AI

Adopting this perspective requires a strategic approach to AI development. It's about building systems that learn the unique "way of speaking" and reasoning that defines your organization.