Limitations of Autoregressive Models and Their Alternatives (2010.11939v3)

Abstract: Standard autoregressive LLMs perform only polynomial-time computation to compute the probability of the next symbol. While this is attractive, it means they cannot model distributions whose next-symbol probability is hard to compute. Indeed, they cannot even model them well enough to solve associated easy decision problems for which an engineer might want to consult a LLM. These limitations apply no matter how much computation and data are used to train the model, unless the model is given access to oracle parameters that grow superpolynomially in sequence length. Thus, simply training larger autoregressive LLMs is not a panacea for NLP. Alternatives include energy-based models (which give up efficient sampling) and latent-variable autoregressive models (which give up efficient scoring of a given string). Both are powerful enough to escape the above limitations.

• The paper highlights fundamental limitations of autoregressive models, showing they cannot model all probability distributions, particularly those requiring NP-hard computations, without impractical parameter growth.
• Under complexity assumptions, autoregressive models need superpolynomial parameters to approximate all string probabilities, making simple scaling impractical for complex tasks.
• Alternative models like Energy-Based Models and latent-variable models offer pathways around limitations but face trade-offs in sampling or scoring efficiency, suggesting a need for hybrid approaches.

Overview of Limitations of Autoregressive Models and Their Alternatives

The paper "Limitations of Autoregressive Models and Their Alternatives" explores the computational efficiency and expressive capacity of autoregressive LLMs, addressing their inherent limitations. These models, which calculate next-symbol probabilities without superpolynomial computational resources, struggle with representing complex probability distributions that require hard computations. The authors argue that empirical advancements with these models might not translate to solving certain language tasks, as they fall short in handling deeply structured decision problems inherent to NLP.

Key Findings and Claims

1. Expressive Limitations: The paper highlights that autoregressive models cannot adequately learn all distributions. For example, they cannot model decision problems that involve computing NP-hard probability distributions associated with the next symbol in a sequence, even if the LLMs are fed extensive computation and data.
2. Complexity Constraints: It becomes apparent that under typical complexity-theoretic assumptions, autoregressive models necessitate superpolynomial growth in parameters to approximate all string probabilities up to length $n$. This constraint points to the impracticality of simply scaling autoregressive models for enhanced performance.
3. Alternative Models: The exploration of Energy-Based Models (EBMs) and latent-variable autoregressive models is significant as they reveal pathways to circumvent some limitations. EBMs, however, sacrifice efficient sampling, and latent-variable models give up efficient scoring, suggesting a trade-off between computational efficiency and modeling power.

Implications

The implications of these findings for both practical applications and theoretical developments in AI and NLP are substantial:

• Modeling Strategy: The paper suggests that relying solely on scaling up autoregressive models might not be the optimal strategy. Instead, researchers might need to explore alternative architectures or hybrid solutions that balance efficiency and expressivity.
• Complex Problem Solving: For addressing complex linguistic problems, energy-based and latent-variable models offer significant potential. Their success in scenarios requiring comprehensive structural understanding highlights a need for incorporating elements that allow for non-trivial, computationally intensive reasoning.

Speculation on Future AI Developments

Given the computational and expressive challenges outlined, future developments in AI could lean more heavily towards hybrid models that effectively integrate the strengths of various architectures. There might be a focus on:

• Enhanced Hybrid Architectures: The blending of strengths from autoregressive methods with latent-variable and energy-based approaches may yield models capable of handling larger, more complex datasets while maintaining operational efficiency.
• Scalable Non-uniform Computations: Addressing the scalability woes through innovations in non-uniform computation strategies that can leverage compact, parameter-efficient models without losing expressive power.
• Algorithmic Efficiency: Further work on efficient approximation algorithms or learning paradigms that approximate complex structures with high fidelity could be central to overcoming current bottlenecks.

In conclusion, the paper is a seminal examination of the inherent limitations of standard autoregressive models and presents a compelling narrative for pursuing alternative modeling approaches to overcome computational and expressive challenges in NLP. This sets a foundation for aspiring towards more capable AI systems that harmonize efficiency with power, ensuring robust solutions to intricate language tasks.