Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs

2025-05-22

AI Summary

This study addresses the challenges of knowledge obsolescence and difficulty in injecting proprietary information into large language models (LLMs). We systematically investigate how task typology affects knowledge retention during injection. Using diverse architectures—including Llama, Qwen, and Phi—we conduct comparative fine-tuning across understanding-oriented tasks (e.g., question answering, cloze) and mapping-oriented tasks (e.g., translation, text-to-JSON). We introduce a dual-axis evaluation framework measuring both knowledge retention rate and cross-context transferability. Our key findings are: (1) depth of cognitive engagement—not data volume—is the primary determinant of injection efficacy; (2) understanding-oriented tasks achieve 48% retention, significantly outperforming mapping-oriented tasks (17–20%), a trend consistent across architectures and aligned with scaling laws; and (3) all models suffer >35% performance degradation in novel contexts, indicating that injected knowledge remains superficial and fails to integrate semantically.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsCognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved informationUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

As the knowledge of large language models (LLMs) becomes outdated over time, there is a growing need for efficient methods to update them, especially when injecting proprietary information. Our study reveals that comprehension-intensive fine-tuning tasks (e.g., question answering and blanks) achieve substantially higher knowledge retention rates (48%) compared to mapping-oriented tasks like translation (17%) or text-to-JSON conversion (20%), despite exposure to identical factual content. We demonstrate that this pattern persists across model architectures and follows scaling laws, with larger models showing improved retention across all task types. However, all models exhibit significant performance drops when applying injected knowledge in broader contexts, suggesting limited semantic integration. These findings show the importance of task selection in updating LLM knowledge, showing that effective knowledge injection relies not just on data exposure but on the depth of cognitive engagement during fine-tuning.

Problem

• Evaluating knowledge transferability in outdated LLMs
• Comparing retention rates across fine-tuning tasks
• Assessing semantic integration of injected knowledge

Innovation

• Comprehension-intensive tasks enhance knowledge retention
• Larger models improve retention across all tasks
• Task selection crucial for effective knowledge injection