Human-like conceptual representations emerge from language prediction (2501.12547v1)

Abstract: Recent advances in LLMs provide a new opportunity to address the long-standing question of how concepts are represented and organized in the mind, which is central to unravelling the nature of human cognition. Here, we reframed the classic reverse dictionary task to simulate human concept inference in context and investigated the emergence of human-like conceptual representations within LLMs. We found that LLMs were able to infer concepts from definitional descriptions and construct representation spaces that converge towards a shared, context-independent structure. These representations effectively predicted human behavioural judgments and aligned well with neural activity patterns in the human brain, offering evidence for biological plausibility. These findings demonstrate that human-like conceptual representations and organization can naturally emerge from language prediction, even without real-world grounding. Our work supports the view that LLMs serve as valuable tools for understanding complex human cognition and paves the way for better alignment between artificial and human intelligence.

Human-like Conceptual Representations Emerge from Language Prediction

The paper examines the intersection of human cognition and artificial intelligence through the lens of LLMs, specifically investigating whether human-like conceptual representations emerge through language prediction tasks. The authors employ a reformulation of the reverse dictionary task to explore the potential of LLMs in simulating human conceptual inference, particularly focusing on the ability of these models to derive and organize concepts in a manner akin to human cognitive processes.

Key Findings

1. Emergence of Human-like Representations: The paper found that LLMs can indeed construct conceptual representation spaces that align with human cognitive structures. Through language prediction tasks, such as the reverse dictionary task, LLMs were able to infer concepts from contextual descriptions and establish representation spaces that increasingly aligned with a shared, context-independent structure.
2. Biological Plausibility: The conceptual representations derived by these models not only predicted human behavioral judgments effectively but also mirrored neural activity patterns observed in the human brain. This alignment offers significant evidence towards the biological plausibility of LLMs as models for understanding human cognition.
3. Contextual Adaptation and Generalization: LLMs demonstrated a capacity to adaptively derive conceptual representations based on contextual information, highlighting the relational interdependencies among concepts. This ability to adapt and generalize fortifies the role of LLMs as tools for investigating the complexities of concept formation and usage in human cognition.
4. Alignment with Human Psychological Measures: The models' representational structures were found to be well-aligned with various psychological measures of human cognition, including similarity judgments, categorization, and feature gradient distinctions. These tasks are fundamental to human concept usage, indicating that LLMs can capture intricate facets of human knowledge.

Methodology

The methodology of the paper revolves around the reverse dictionary task, adapted to evaluate LLMs in a domain-general approach to concept inference. The authors harnessed the capabilities of these models’ in-context learning by providing minimal context through random demonstrations. The research assesses how well LLM-derived representational structures map onto psychological and brain data by performing rigorous evaluations across thousands of naturalistic object concepts.

Implications

The implications of these findings extend to both theoretical and practical domains:

• Theoretical Implications: The ability of LLMs to develop human-like conceptual structures through next-token prediction challenges traditional views on the necessity of real-world grounding for concept formation. The paper suggests a convergence toward a shared conceptual understanding, advocating for the hypothesis that AI systems can eventually mirror human cognitive structures over time and complexity scales.
• Practical Implications: LLMs as tools for cognitive science could pave the way for enhancing AI systems' alignment with human intelligence. This will likely lead to more intuitively interacting AI systems, with improved capabilities in understanding and collaborating with humans across various contexts and tasks.

Future Directions

The paper opens avenues for further research into enhancing the alignment of LLMs with human cognition. This might involve:

• Integrating multi-modal data sources (e.g., visual, auditory) to enrich the conceptual representations of LLMs and align them more closely with human-like reasoning and understanding.
• Exploring the use of LLMs in more complex reasoning and decision-making processes to further examine the parallels and gaps between computationally-driven and biologically-inspired cognition.
• Investigating the potential of these models in multidisciplinary domains, where the understanding and interaction of AI with human users can lead to breakthroughs in collaborative tasks and interface design.

In conclusion, this paper underscores the potential of LLMs not only as computational models for natural language processing but also as valuable probes into the foundations of cognitive science. The emergence of human-like conceptual structures within LLMs suggests a significant step towards bridging the gap between artificial systems and human minds.