Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences (2410.21332v2)

Abstract: Humans excel at learning abstract patterns across different sequences, filtering out irrelevant details, and transferring these generalized concepts to new sequences. In contrast, many sequence learning models lack the ability to abstract, which leads to memory inefficiency and poor transfer. We introduce a non-parametric hierarchical variable learning model (HVM) that learns chunks from sequences and abstracts contextually similar chunks as variables. HVM efficiently organizes memory while uncovering abstractions, leading to compact sequence representations. When learning on language datasets such as babyLM, HVM learns a more efficient dictionary than standard compression algorithms such as Lempel-Ziv. In a sequence recall task requiring the acquisition and transfer of variables embedded in sequences, we demonstrate HVM's sequence likelihood correlates with human recall times. In contrast, LLMs struggle to transfer abstract variables as effectively as humans. From HVM's adjustable layer of abstraction, we demonstrate that the model realizes a precise trade-off between compression and generalization. Our work offers a cognitive model that captures the learning and transfer of abstract representations in human cognition and differentiates itself from LLMs.

• The paper introduces HVM, a hierarchical model that efficiently chunks and abstracts data sequences to improve generalization.
• It combines a probabilistic generative framework with cognitive chunking techniques to mimic human-like abstraction and memory processes.
• Empirical evaluations using synthetic and real-world data show HVM outperforms standard models in compression efficiency and learning accuracy.

Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences

The paper presents a novel cognitive model called the Hierarchical Variable Model (HVM) for learning and transferring abstract representations from sequences of data. The HVM seeks to bridge the gap observed in current artificial intelligence models, particularly LLMs, which struggle with tasks requiring deep abstraction, often relying more on associative learning than true abstraction.

Model Framework

The HVM introduces a non-parametric hierarchical modeling framework that works by chunking sequences and abstracting these chunks into variables. This dual mechanism enables the model to generate a compact, memory-efficient representation of observed data sequences. By doing so, HVM can efficiently process and transfer learned patterns to new, unfamiliar sequences, closer to the human cognitive process.

Key Contributions

1. Generative Model: The paper provides a probabilistic generative model equipped with hierarchical structures, which mirrors real-world analogues such as linguistic sequences or chemical compounds. From an initial set of atomic units, the model creates new objects or categories through a recursive expansion process. The observational sequences derived from such a model are nests of hierarchies mimicking natural abstractions.
2. Chunking and Abstraction: By building on previous chunking models, this work proposes combining chunking with abstraction within a unified system. HVM abstracts by identifying shared features among observed entities, aligning more closely with how variables function within programming paradigms.
3. Empirical Evaluation: Through experiments using both synthetically generated sequences and real-world text from the BabyLM dataset, HVM demonstrates significant improvements. It surpasses standard models like LZ78, both in compression efficiency and in learning accuracy. The evaluation focuses on sequence length post-parsing, sequence likelihood, and compressing dictionary sizes, showing that HVM leads in generating sequences of maximal likelihood and minimal description length.
4. Human-like Abstraction: During a sequence memory task, HVM's ability to replicate human abstraction and recall processes was tested. The model's sequence likelihood positively correlates with human recall times even in transfer blocks, suggesting that HVM encapsulates human-like generalization capabilities.
5. Comparative Analysis with LLMs: The paper compares HVM with several state-of-the-art LLMs, exposing the discrepancy in abstraction learning capabilities. The LLMs, while capable of rudimentary reasoning, failed to replicate the abstraction and generalization observed in human-like models. HVM, on the other hand, achieves better abstraction, affirming its role in manifesting human-like cognition.

Implications

Theoretically, the HVM contributes to understanding cognitive abstraction by formalizing a strategy that combines chunking and abstraction—traditionally explored separately—as inherent parts of the same model. The trade-off between compression efficiency and uncertainty introduced by learned abstractions is elucidated in terms of rate-distortion theory, offering a novel lens through which to paper cognitive processes and artificial learning models.

Practically, the results imply potential applications in developing AI systems capable of more human-like understanding and reasoning. The HVM’s unique approach may inform future architectures for models that can leverage hierarchical structures more effectively, particularly for applications requiring nuanced contextual understanding and generalization from limited data.

Future Directions

The paper identifies certain limitations, such as the restriction where variables cannot appear at the sequence's start or end and the reliance on previously learned representations for future abstractions. Future work could aim at refining these constraints and enhancing the model's computational efficiency for specific applications. Further investigation into the connection between abstraction versatility, generalization capabilities, and real-world performance in AI models is essential for advancing these research avenues.

The contribution of this paper delineates a significant stride toward more cognitively aligned AI systems by merging cognitive insight with technical innovations in machine learning. The HVM stands as a sophisticated tool for both AI and cognitive science, advancing our understanding of abstract representation learning.