The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models (2505.24874v1)

Abstract: Neuro-symbolic learning was proposed to address challenges with training neural networks for complex reasoning tasks with the added benefits of interpretability, reliability, and efficiency. Neuro-symbolic learning methods traditionally train neural models in conjunction with symbolic programs, but they face significant challenges that limit them to simplistic problems. On the other hand, purely-neural foundation models now reach state-of-the-art performance through prompting rather than training, but they are often unreliable and lack interpretability. Supplementing foundation models with symbolic programs, which we call neuro-symbolic prompting, provides a way to use these models for complex reasoning tasks. Doing so raises the question: What role does specialized model training as part of neuro-symbolic learning have in the age of foundation models? To explore this question, we highlight three pitfalls of traditional neuro-symbolic learning with respect to the compute, data, and programs leading to generalization problems. This position paper argues that foundation models enable generalizable neuro-symbolic solutions, offering a path towards achieving the original goals of neuro-symbolic learning without the downsides of training from scratch.

Overview of Neuro-Symbolic Learning with Foundation Models

The paper, "The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models," discusses the evolving landscape of neuro-symbolic learning (NSL) by integrating foundation models. NSL is traditionally positioned to provide interpretable, reliable, and efficient solutions for complex reasoning tasks. However, training these models from scratch has often led to scalability challenges and generalization issues, limiting their ability to handle intricate problems. This paper contends that the advent of foundation models reshapes the NSL paradigm by replacing the need for specialized training with the more versatile and powerful approach of neuro-symbolic prompting.

Key Findings

The authors identify three major pitfalls inherent in the traditional NSL approach:

1. Compute Pitfall: As foundation models scale, their performance can match or exceed that of specialized neural models in NSL settings. Through experimentation across benchmarks like Sum5, HWF5, CLUTRR, Leaf, and CLEVR, the paper illustrates that foundation models, without any additional training, can achieve comparable or superior results by leveraging their extensive pretrained capabilities. The diminishing performance gap emphasizes the inefficacy of spending computational resources on training specialized neural models within NSL systems.
2. Data Pitfall: The authors argue that traditional NSL methods often learn to overfit to specific dataset biases rather than generalizable features. By comparing performance under controlled noise addition, foundation models proved more robust than trained NSL models, supporting the assertion that foundation models provide stronger generalization.
3. Program Pitfall: In NSL systems, the symbolic reasoning program provides weak supervision to the neural perception models, which can result in them hallucinating symbols. When the foundation models fail, it is often due to ambiguous inputs where the NSL models happen to reach the correct result through incorrect intermediate symbols, exposing flawed reliance on fixed reasoning programs.

These findings collectively call into question the continued need for specialized training in NSL, advocating instead for the use of foundation models through neuro-symbolic prompting.

Implications and Future Directions

The implications of adopting foundation models within NSL are substantial. Firstly, the ability to utilize foundation models for general perception tasks without requiring robust labeled datasets increases the practicality and applicability of NSL systems across domains. Furthermore, symbolic programs within neuro-symbolic prompting setups can still ensure high reliability and precise computational tasks, reinforcing the position that explicit symbolic representation should be coupled with foundation models for tasks involving intricate reasoning and computation.

Looking forward, the paper highlights the synthesis of symbols and programs autonomously as the critical frontier for NSL. This transition necessitates overcoming the limitations of predetermined symbolic programs to harness the true potential of foundation models for dynamic and versatile reasoning. Industry interest in integrating code generation and execution with foundation models demonstrates the momentum towards such capabilities.

Conclusion

This paper's position marks a pivotal shift in thinking about neuro-symbolic learning by highlighting the advantages of leveraging foundation models. By identifying major pitfalls of traditional NSL and discussing their implications, it underscores the importance of evolving NSL methodologies towards greater reliance on foundation models, contrasting the conventional reliance on specialized training with prompting approaches. The insights provide a valuable perspective for researchers exploring the application of NSL in real-world scenarios where generalizable, interpretable, and reliable AI systems are increasingly sought after.