The Rosetta Paradox: Domain-Specific Performance Inversions in Large Language Models
Basab Jha, Ujjwal Puri
TL;DR
The paper addresses the counterintuitive Rosetta Paradox, where large language models (LLMs) exhibit strong performance in specialized knowledge domains yet underperform on general tasks. It introduces two metrics, the Domain Specificity Index ($DSI$) and the Performance Inversion Metric ($PIM$), within a cross-domain evaluation framework to quantify domain-specific behavior in LLMs. Through experiments across models such as GPT-3, BioBERT, and LEGAL-BERT, it shows that domain-specialized models achieve high accuracy in their niches but degrade on general tasks, while general models tend to be more balanced, illustrating intrinsic trade-offs rather than data artifacts. The paper discusses potential causes (data biases, catastrophic forgetting, architectural biases, emergent properties) and proposes mitigation strategies including balanced pre-training, domain-adaptive fine-tuning, continual learning, and cross-domain knowledge integration, while outlining future directions, ethical considerations, and the development of Rosetta Paradox-aware AI systems and evaluation frameworks.
Abstract
While large language models, such as GPT and BERT, have already demonstrated unprecedented skills in everything from natural language processing to domain-specific applications, there came an unexplored phenomenon we term the Rosetta Paradox. The Rosetta Paradox characterizes the counterintuitive performance inversions across domains of knowledge. This paradox captures how such LLMs can excel in highly specialized fields but do poorly on tasks which require general, everyday knowledge. This paper formalizes the definition of the Rosetta Paradox and introduces a panoramic analysis framework that includes both a Domain Specificity Index (DSI) and a Performance Inversion Metric (PIM) for consistent quantification of domain-specific behavior in LLMs. We adopt this paradox and conduct a series of investigations through extensive experiments across diverse models and knowledge domains, ranging from rich technical areas to common-sense reasoning. Our findings indicate that the Rosetta Paradox is likely not a mere artifact of data distribution but an intrinsic architectural and emergent property of deep neural networks. We present comparative analyses across different model architectures, sizes, and training methodologies that shed light into the peculiar ways this paradox manifests itself and challenge the standard evaluation metrics.