Meaning without reference in large language models (2208.02957v2)

Abstract: The widespread success of LLMs has been met with skepticism that they possess anything like human concepts or meanings. Contrary to claims that LLMs possess no meaning whatsoever, we argue that they likely capture important aspects of meaning, and moreover work in a way that approximates a compelling account of human cognition in which meaning arises from conceptual role. Because conceptual role is defined by the relationships between internal representational states, meaning cannot be determined from a model's architecture, training data, or objective function, but only by examination of how its internal states relate to each other. This approach may clarify why and how LLMs are so successful and suggest how they can be made more human-like.

Meaning without Reference in LLMs

The paper "Meaning without Reference in LLMs" by Piantadosi and Hill scrutinizes the assumption that LLMs inherently lack human-like understanding due to their lack of reference to real-world objects. This paper challenges the prevalent skepticism by advancing the argument that LLMs might already encapsulate facets of human-like meaning reflective of conceptual roles theorized in cognitive science and philosophy of language. The paper concludes that while LLMs may not grasp meaning in entirety akin to humans, they establish a foundational approximation of the conceptual interrelations that are pivotal for meaning.

LLMs and Conceptual Roles

The principal contention revolves around the inadequacy of equating meaning purely with reference and the emphasis on conceptual role theory. Conceptual roles delineate meaning not by associations to tangible objects, but through the interaction and interrelation of internal cognitive or representational states. This understanding implies that the meaning is an emergent property grounded in the network of internal relationships rather than in reference alone. In the field of LLMs, this suggests that their meaning-related capabilities should be inferred from an analysis of their internal state interactions, rather than from their architectural design or training data.

Philosophical precedents such as Wittgenstein's considerations on non-referential words (e.g., "justice") and Piantadosi's church-encoding in neural networks provide a theoretical scaffold for understanding how LLMs can ascribe meaning through internal relationships. Highlighted is that many human concepts and terminologies, historically devoid of fixed referential anchors, acquire meaning through structured interrelations in cognitive domains.

Empirical Observations

Empirical evidence demonstrates that LLMs like transformers manage to approximate the structures of human conceptual relationships. Notably, studies on representational geometry inside these models show qualitative parallels with humans' cognitive processes. Examples from tasks involving analogical reasoning, narrative coherence, factual question-response, and logical reasoning underscore LLMs' capacities for maintaining intricate relationships between concepts. Larger LLM architectures tend to exhibit improved alignment with human data, aligning more closely with the nuanced and gradient distinctions inherent in human semantic cognition.

Moreover, applications such as AlphaFold demonstrate the ability of transformer models to deduce complex relationships from seemingly simplistic training regimes, revealing latent structural patterns and interactions within biochemical domains. This analogy bolsters the argument that foundational aspects of meaning, emergent by nature, are encoded within LLMs through their training on linguistic data.

Challenges and Directions

Acknowledged within the analysis are the ongoing limitations confronting current LLM capabilities, including the requirement for enhancing compositionality, abstract reasoning, and grounded reference to attain more robust semantic modeling and richer human-like language understanding. The necessity of multimodal training data and the embedding of dynamic environmental feedback loops is underlined as potential avenues for advancement.

The article restrains from claiming that LLMs wholly mimic human semantic comprehension, but posits that reference alone is not a singularly sufficient determinant of meaning. Instead, reference should be considered an optional augment, relevant to some concepts just as other properties such as color or teleology might be relevant to others.

Implications

This discourse suggests considerable theoretical ramifications on how LLM progress aligns with our broader understanding of cognition and language. As AI models continue evolving, exploring ways to enrich the inter-linked conceptual frameworks that underpin their language processing will be vital. Further integration with alternative sensory data and cognitive architectures promises to bridge existing gaps between LLM operations and human linguistic experience, advancing both practical applications and theoretical insights into the nature of communication and understanding in artificial systems.

Overall, this paper invites a reevaluation of entrenched assumptions regarding meaning-making capabilities in LLMs and suggests that continued research into their conceptual structures holds promise for more nuanced and potent language technologies.