Large Concept Models: Language Modeling in a Sentence Representation Space (2412.08821v2)

Abstract: LLMs have revolutionized the field of artificial intelligence and have emerged as the de-facto tool for many tasks. The current established technology of LLMs is to process input and generate output at the token level. This is in sharp contrast to humans who operate at multiple levels of abstraction, well beyond single words, to analyze information and to generate creative content. In this paper, we present an attempt at an architecture which operates on an explicit higher-level semantic representation, which we name a concept. Concepts are language- and modality-agnostic and represent a higher level idea or action in a flow. Hence, we build a "Large Concept Model". In this study, as proof of feasibility, we assume that a concept corresponds to a sentence, and use an existing sentence embedding space, SONAR, which supports up to 200 languages in both text and speech modalities. The Large Concept Model is trained to perform autoregressive sentence prediction in an embedding space. We explore multiple approaches, namely MSE regression, variants of diffusion-based generation, and models operating in a quantized SONAR space. These explorations are performed using 1.6B parameter models and training data in the order of 1.3T tokens. We then scale one architecture to a model size of 7B parameters and training data of about 2.7T tokens. We perform an experimental evaluation on several generative tasks, namely summarization and a new task of summary expansion. Finally, we show that our model exhibits impressive zero-shot generalization performance to many languages, outperforming existing LLMs of the same size. The training code of our models is freely available.

• The paper introduces a paradigm shift by modeling language at the sentence level to enhance semantic representation and hierarchical reasoning.
• It details methodologies such as MSE regression, diffusion-based techniques, and quantized SONAR space for training on trillions of tokens across 200 languages.
• Experimental results demonstrate superior zero-shot generalization on tasks like summarization compared to traditional token-based LLMs.

Overview of Large Concept Models (LCM)

The paper at hand, "Large Concept Models: LLMing in a Sentence Representation Space," explores an innovative approach to LLMing that diverges significantly from conventional token-level processing. The authors introduce the concept of modeling at a higher semantic level—termed "concepts"—which represent an abstraction superior to individual tokens. This approach is inherently language- and modality-agnostic, offering a potential pathway for improved reasoning and planning capabilities in AI systems beyond the limitations of traditional LLMs.

Core Concepts

The paper proposes a Large Concept Model (LCM) that utilizes sentence embeddings rather than token sequences. The researchers leverage the SONAR sentence embedding space, which supports 200 languages in both text and speech modalities, to facilitate a proof of feasibility for their LCM approach. The concept of a sentence as the atomic unit is evaluated for its potential to handle language-independent semantic representations, contrasting the token-centric methodologies prevalent in current LLMs like GPT and LLaMA.

Methodological Exploration

The authors meticulously explore various methodologies for training LCMs, including:

• MSE Regression: Minimizing the Mean Squared Error for next concept prediction.
• Diffusion-Based Approaches: Implementing variants grounded in diffusion models to manage the conditional probability distributions of embeddings.
• Quantized SONAR Space: Leveraging quantization for operating in a discretized embedding space, which facilitates handling the inherent continuous nature of sentence embeddings efficiently.

These models were trained using a substantial dataset encompassing trillions of tokens, with detailed experimentation conducted on architectures scaling up to 7 billion parameters.

Experimental Findings

The paper reports a comprehensive evaluation of the LCM's performance on tasks like summarization and a novel summary expansion task. Strong numerical results highlight the impressive zero-shot generalization capabilities of LCMs, particularly across a multitude of languages, where they outperformed existing LLMs of comparable size. The provided experimental data firmly demonstrate the potential of LCMs in extending the capabilities of AI models beyond token-based processing.

Theoretical and Practical Implications

From a theoretical standpoint, the LCM approach suggests a paradigm shift that could lead to more effective hierarchical reasoning in AI. By abstracting language processing to a semantic level, the potential for enhanced coherence in long-form output is significant. Practically, the modular and extensible design of LCMs promises improved scalability across languages and modalities. However, the paper underscored the current limitations associated with employing fixed-size sentence embeddings, such as handling exceptionally long or complex sentences.

Future Prospects

Further research directions include refining the concept embedding space (potentially beyond SONAR) and developing more efficient training architectures that can harness the full power of hierarchical structure learning. Exploration into more generalized embedding spaces and the integration of LCMs with existing AI systems could unlock advancements in areas like multilingual understanding and cross-modal reasoning.

In conclusion, this paper presents a compelling new approach to LLMing that emphasizes semantic processing over token-based prediction. It lays a foundational understanding for future research aiming to close the gap between human-like reasoning and AI capabilities.