Core Concepts
The study explores the impact of task-switches on conversational large language models, revealing vulnerabilities and performance degradation.
Abstract
The study investigates how task-switches affect conversational history in large language models. It highlights the risks and impacts of task-switches on model performance across various datasets. The findings suggest that different models exhibit varying levels of sensitivity to task-switches, influencing their adaptability and response quality.
Large language models (LLMs) have been widely deployed in conversational systems, showcasing remarkable capabilities. In-context learning enhances LLM responses by leveraging conversation history. However, the study reveals that task-switches can lead to significant performance degradation in LLMs. The research formalizes the risk of performance decline due to task-switches and presents insights into the sensitivity of popular LLMs to different tasks.
The experiments conducted across multiple datasets demonstrate how task-switches affect model performance. Different LLMs show varying degrees of vulnerability to task-switches, impacting their ability to seamlessly switch between tasks without compromising performance. The study lays the groundwork for future research on mitigating risks associated with task-switch sensitivity in LLMs.
Stats
Our work makes the first attempt to formalize the study of vulnerabilities and interference caused by task-switches in conversational large language models.
Experiments reveal that many task-switches can lead to significant performance degradation.
Different datasets covering a range of tasks were evaluated using popular LLMs.
Sensitivity metrics were used to measure the impact of task-switches on model performance.
Performance changes and format errors were analyzed across various conversation history lengths.