arXiv:2602.03542v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained and tested extensively on symbolic representations such as code and graphs, yet real-world user tasks are often specified in natural language. To what extent can LLMs generalize across these representations? Here, we approach this question by studying isomorphic tasks involving procedures represented in code, graphs, and natural language (e.g., scheduling steps in planning). We find that training LLMs with popular post-training methods on graphs or code data alone does not reliably generalize to
The rapid advancement and widespread deployment of Large Language Models necessitate a deeper understanding of their fundamental capabilities across diverse data representations.
Understanding LLMs' generalization across symbolic and natural language representations is critical for developing more robust, reliable, and user-friendly AI systems that bridge the gap between technical models and real-world applications.
This research highlights limitations in current LLM generalization abilities between formal and informal representations, suggesting a need for modified training paradigms to achieve broader applicability.
- · AI researchers focusing on generalization
- · Developers creating multimodal AI applications
- · Companies investing in advanced LLM training techniques
- · Platforms relying solely on symbolic LLM training for natural language tasks
- · Approaches assuming seamless generalization across data types
- · Users encountering unreliable or inconsistent LLM performance
The findings will directly inform the development of next-generation LLM architectures and training methodologies focused on cross-representation generalization.
Improved generalization could lead to more efficient and versatile AI agents capable of executing complex instructions specified in natural language across various digital domains.
This enhancement in AI agent capability might accelerate the automation of white-collar tasks, further impacting labor markets and requiring new skill sets.
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Read at arXiv cs.LG