Logical reasoning failure - Do we expect AGI instead of LLM?

Understanding the limits of inference in large language models - Recent progress and ongoing challenges

Apple's recent paper, GSM-Symbolic: Understanding the Limits of Mathematical Reasoning in Large Language Models, has sparked widespread discussion about the reasoning capabilities of state-of-the-art language models (LLMs), particularly those based on transformer architectures such as GPT (source). While much of the debate centres on the validity of benchmarks and methodologies, it is important for practitioners to understand the practical limitations of these models beyond academic evaluations. Furthermore, the implications of the EU AI Act highlight the importance of implementing robust quality management systems in development processes, which can help prevent issues such as data contamination.

A critical concern arises when LLMs are framed as components of Artificial General Intelligence (AGI). While LLMs can simulate conversational intelligence, their reasoning is fundamentally pattern-based rather than logic-driven. Human cognitive biases, such as pareidolia, lead us to perceive intelligence in interactive systems even in the absence of true reasoning. It is therefore crucial to understand both the strengths and weaknesses of these tools, and to recognise our own biases in interpreting their output.

In real-world applications, LLMs serve both direct and indirect purposes. Direct applications involve the generation or refinement of text using the model's ability to produce coherent and contextually appropriate content. For these tasks, current models have proved highly effective, making some academic criticisms less relevant in practical settings.

Indirect applications, however, involve interpreting real-world input and interacting with other systems, including self-referential processing. Whether the model misinterprets input or makes incorrect decisions - such as incorrect searches or inappropriate use of integrated tools via APIs - these limitations in reasoning, as highlighted by the Apple study, can have tangible consequences. Nevertheless, LLMs primarily map input to pre-learned patterns, a process that serves many applications effectively when carefully managed by human operators.

Rather than focusing on workarounds, it is more productive to address the root causes of these limitations.

Key findings from the GSM Symbolic paper

Apple's GSM symbolic benchmark exposes critical weaknesses in the reasoning capabilities of LLMs:

Data contamination: Models perform significantly better on the original GSM8K benchmark due to potential overlap with their training data, raising concerns about overestimated reasoning abilities.

Sensitivity to input variations: Changing numerical values or adding irrelevant clauses to questions leads to severe performance drops - up to 65% - highlighting a reliance on pattern matching rather than true understanding.

Complexity handling: Increasing the complexity of questions by adding more clauses degrades model performance, highlighting difficulties with multi-step reasoning.

Implications for AI development and regulation

These findings underscore the importance of rigorous evaluation and quality management in AI development. Models should be tested against diverse, contamination-resistant benchmarks to ensure reliable deployment.

The EU AI Act and similar regulations should be seen as an opportunity rather than an obstacle. These frameworks encourage thoughtful development practices that promote safer and more reliable AI systems. Implementing systematic quality management processes is in line with best practices for developing mission-critical and highly regulated products - an area in which I specialise.

Final thoughts

Pre-trained models are powerful tools when used thoughtfully. Understanding the interplay between fine-tuning datasets and underlying models is essential. Benchmarks and requirements should accurately reflect the intended use cases.

If you're interested in integrating cutting-edge models into your products or establishing effective quality management systems in your development pipeline, feel free to reach out.

Petr Švimberský