Learned but Not Expressed: Capability-Expression Dissociation in Large Language Models
arXiv:2603.18013v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate the capacity to reconstruct and trace learned content from their training data under specific elicitation conditions, yet this capability does not manifest in standard generation contexts. This empirical observational study examines the expression of non-causal, non-implementable solution types across 300 prompt-response generations spanning narrative and problem-solving task contexts. Drawing on recent findings regarding memorization contiguity and alignment-induced discourse priors, we document a systematic dissociation between learned capability and expressed output. Across three distinct LLMs, ten task scenarios, and both creative narrative and practical advisory contexts, we documented zero instances of non-causal solution frames in generated outputs (0%, 95% CI: [0%, 1.2%]), despite verified reconstruction capability under conditional extraction. These findings challenge the prevailing assum
arXiv:2603.18013v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate the capacity to reconstruct and trace learned content from their training data under specific elicitation conditions, yet this capability does not manifest in standard generation contexts. This empirical observational study examines the expression of non-causal, non-implementable solution types across 300 prompt-response generations spanning narrative and problem-solving task contexts. Drawing on recent findings regarding memorization contiguity and alignment-induced discourse priors, we document a systematic dissociation between learned capability and expressed output. Across three distinct LLMs, ten task scenarios, and both creative narrative and practical advisory contexts, we documented zero instances of non-causal solution frames in generated outputs (0%, 95% CI: [0%, 1.2%]), despite verified reconstruction capability under conditional extraction. These findings challenge the prevailing assumption that training data presence directly predicts output probability, demonstrating instead that task-conditioned generation policies can comprehensively suppress learned content across diverse contexts. The results offer implications for understanding generation dynamics, output distribution control, and the behavioral boundaries of contemporary LLMs.
Executive Summary
This study investigates the expression of non-causal, non-implementable solution types in large language models (LLMs) through empirical observations across 300 prompt-response generations. The findings reveal a systematic dissociation between learned capability and expressed output, challenging the assumption that training data presence directly predicts output probability. This study demonstrates that task-conditioned generation policies can comprehensively suppress learned content across diverse contexts, offering implications for understanding generation dynamics and output distribution control. The results also highlight the importance of examining the behavioral boundaries of contemporary LLMs. By shedding light on the complex relationship between learned content and expressed output, this study contributes to the ongoing discussion on the capabilities and limitations of LLMs.
Key Points
- ▸ Large language models demonstrate capability to reconstruct learned content, but this is not reflected in standard generation contexts.
- ▸ A systematic dissociation between learned capability and expressed output is observed across various LLMs, tasks, and contexts.
- ▸ Task-conditioned generation policies can suppress learned content, challenging the assumption that training data presence directly predicts output probability.
Merits
Strength
The study's use of a large-scale dataset and multi-context approach provides a comprehensive understanding of LLMs' expression of non-causal solution types.
Strength
The findings contribute to the ongoing discussion on the capabilities and limitations of LLMs, highlighting the importance of examining their behavioral boundaries.
Demerits
Limitation
The study's focus on a specific type of solution frames (non-causal, non-implementable) may limit its generalizability to other types of learned content.
Expert Commentary
This study highlights the importance of examining the complex relationship between learned content and expressed output in LLMs. The findings suggest that task-conditioned generation policies can have a profound impact on the expression of learned content, and that this can be both beneficial and limiting. As LLMs continue to be developed and deployed in various contexts, it is essential to understand the behavioral boundaries of these models and to develop more effective generation policies and output distribution control mechanisms. This study's contribution to the ongoing discussion on the capabilities and limitations of LLMs is significant, and its implications for the development and deployment of AI-powered systems are far-reaching.
Recommendations
- ✓ Future research should investigate the generalizability of the study's findings to other types of learned content and LLM architectures.
- ✓ Developers and users of LLMs should prioritize the development of more effective generation policies and output distribution control mechanisms to ensure responsible AI development and use.
