2024 LargeConceptModelsLanguageModel
- (LCM team et al., 2024) ⇒ LCM team, Loïc Barrault, Paul-Ambroise Duquenne, Maha Elbayad, Artyom Kozhevnikov, Belen Alastruey, Pierre Andrews, Mariano Coria, Guillaume Couairon, Marta R. Costa-jussà, David Dale, Hady Elsahar, Kevin Heffernan, João Maria Janeiro, Tuan Tran, Christophe Ropers, Eduardo Sánchez, Robin San Roman, Alexandre Mourachko, Safiyyah Saleem, and Holger Schwenk. (2024). “Large Concept Models: Language Modeling in a Sentence Representation Space.” doi:10.48550/arXiv.2412.08821
Subject Headings: Sentence Embedding System, Diffusion-Based Generation, Zero-Shot Multilingual Generalization, Concept-Level Hierarchical Language Modeling.
Notes
- Sentence-Level Architecture: The paper proposes a novel Large Concept Model (LCM) that shifts from word-level token prediction to sentence-level “concept” embeddings, aiming to reflect more natural human reasoning at higher granularity.
- Diffusion-Based Generation: The paper explores continuous generative modeling for sentences through diffusion processes, showing how noisy embeddings can be iteratively denoised into coherent sentence representations.
- Quantized Embedding: The authors investigate a discrete alternative to diffusion by quantizing SONAR embeddings with Residual Vector Quantization, enabling discrete sampling at the sentence level, albeit with potential trade-offs in quality.
- Multilingual Coverage: Leveraging SONAR’s encoder-decoder pipeline, the paper highlights support for up to 200 languages and multiple modalities, making the LCM language-agnostic and modality-agnostic in its core reasoning.
- Zero-Shot Generalization: Even though the LCM is trained primarily on English text, it demonstrates effective zero-shot transfer to numerous other languages via SONAR’s universal sentence representation space.
- Long-Context Summarization: The paper evaluates LCM on tasks like CNN/DailyMail, XSum, and LCFO, illustrating how operating on sentence embeddings can handle extended contexts efficiently and produce coherent multi-sentence summaries.
- Summary Expansion: The authors propose a “reverse” summarization scenario, where the model elaborates short summaries into richer, longer text, showcasing the LCM’s creative generation capabilities.
- Hierarchical Planning: Preliminary experiments incorporate paragraph-level “plan concepts,” suggesting that explicit high-level outlines or planning steps can improve coherence and narrative structure in long-form generation.
- Efficiency in Large Contexts: The paper argues that LCMs can be more cost-effective when handling thousands of tokens, because sentence embeddings drastically reduce the sequence length compared to token-level transformers.
- Limitations and Future Work: The paper acknowledges challenges in out-of-distribution embeddings, fragility of certain sentence representations, and the need for custom concept spaces. It also calls for further research on [[hierarchical archit
Cited By
Quotes
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.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024 LargeConceptModelsLanguageModel | Holger Schwenk LCM team Paul-Ambroise Duquenne Maha Elbayad Artyom Kozhevnikov Belen Alastruey Pierre Andrews Mariano Coria Guillaume Couairon David Dale Hady Elsahar Kevin Heffernan Tuan Tran Christophe Ropers Robin San Roman Alexandre Mourachko Safiyyah Saleem Loïc Barrault Marta R. Costa-jussà João Maria Janeiro Eduardo Sánchez | Large Concept Models: Language Modeling in a Sentence Representation Space | 10.48550/arXiv.2412.08821 | 2024 |