large language models Creating connections between symbols

Large Language Models (LLMs) have made significant strides in natural language processing, but their ability to create connections between symbols-particularly in the context of symbolic reasoning and manipulation-remains a complex and evolving area of research. Below, I explore how LLMs handle symbolic tasks, their challenges, and emerging methods to improve their performance in creating meaningful connections between symbols.

LLMs, such as GPT-4 and various open-source models, are primarily designed for language-based tasks, leveraging vast datasets to generate coherent text. However, their capacity to handle symbolic tasks-such as mathematical operations, logical reasoning, and spatial planning-has been a focal point of recent studies. Research indicates that LLMs struggle with context-free and context-sensitive symbolic tasks as complexity increases, particularly when the number of symbols grows. For instance, tasks involving addition, multiplication, and symbolic counting reveal a decline in performance with larger symbol sets, even in fine-tuned models like GPT-3.5 24.

A key challenge is that LLMs often fail to learn underlying rules of symbol manipulation, instead relying on pattern recognition or tuple-based relationships derived from their training data. This limits their generalization ability in symbol-intensive scenarios, suggesting that their architecture may not be inherently suited for pure symbolic reasoning without additional modifications or training 2.

One major issue is the representation of symbols within LLMs. Unlike humans, who can abstract and manipulate symbols based on learned rules, LLMs often treat symbols as part of linguistic patterns rather than as independent entities with inherent relationships. Studies argue that LLMs lack a true symbolic grounding, meaning they do not possess an internal mechanism to represent symbols as distinct, manipulable concepts outside of language contexts 1. This is evident in their inconsistent performance on tasks requiring precise order of operations or preservation of symbolic structures 2.

Moreover, when dealing with spatial reasoning-a domain heavily reliant on symbolic connections-LLMs exhibit notable deficiencies. For example, when tasked with understanding virtual spatial environments described in natural language, models struggle to accurately interpret and act upon spatial relationships, highlighting a gap in their ability to connect symbols meaningfully in non-linguistic contexts 57.

Despite these challenges, several innovative approaches are being developed to improve LLMs' ability to create connections between symbols:

• : This method converts complex spatial relationships or environments into condensed symbolic representations during intermediate reasoning steps. Unlike traditional Chain-of-Thought (CoT) prompting, which relies on natural language descriptions, CoS uses simple symbols to represent relationships (e.g., "/" for "on top of"). Experiments show that CoS significantly outperforms CoT, with accuracy gains of up to 60.8% on spatial planning tasks like Brick World for GPT-3.5-Turbo, while also reducing token usage by up to 65.8%. This suggests that symbolic representations can be more efficient for LLMs in certain contexts 57.

• : Another promising approach involves translating symbolic representations into language-based inputs that LLMs can more easily process. By converting symbols into descriptive text, S2L enhances LLMs’ comprehension of symbol-related problems. For instance, applying S2L with GPT-4 resulted in accuracy improvements of 21.9% on 1D-ARC tasks and 9.5% on Dyck language tasks, alongside consistent gains in other symbol-related challenges like table understanding. This method bridges the gap between symbolic and linguistic processing without requiring model retraining 6.

• : The convergence of connectionist (neural network-based) and symbolic AI paradigms offers a broader framework for enhancing symbolic connections. LLM-empowered Autonomous Agents (LAAs) integrate symbolic subsystems with neural architectures, enabling better reasoning and decision-making. This hybrid approach mimics human-like reasoning processes and scales effectively with large datasets, providing a potential pathway for LLMs to handle symbolic tasks more adeptly 3.

The current limitations of LLMs in creating connections between symbols underscore the need for specialized training, architectural adjustments, and novel prompting techniques. Research suggests that developing larger models capable of learning automata-rather than merely storing information as tuples-could improve symbolic reasoning 2. Additionally, exploring neuro-vector-symbolic integration and implicit reasoning methods may further enhance LLMs’ capabilities in this domain 3.

In summary, while LLMs face significant hurdles in symbolic reasoning and connecting symbols, emerging strategies like CoS prompting, S2L conversion, and neuro-symbolic integration are paving the way for substantial improvements. These advancements highlight the potential for LLMs to evolve beyond language processing into more abstract, symbol-driven tasks, though much work remains to achieve true symbolic grounding.

Citations:

1. https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0041
2. https://arxiv.org/html/2405.13209v1
3. https://arxiv.org/html/2407.08516v3
4. https://arxiv.org/pdf/2405.13209.pdf
5. https://arxiv.org/html/2305.10276v7
6. https://aclanthology.org/2025.coling-main.372.pdf
7. https://openreview.net/forum?id=Hvq9RtSoHG
8. https://en.wikipedia.org/wiki/Large_language_model
9. https://stackoverflow.blog/2023/07/03/do-large-language-models-know-what-they-are-talking-about/
10. https://aclanthology.org/2024.acl-long.707.pdf

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Answer from Perplexity: pplx.ai/share