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LLM Task Interference: an Initial Study on the Impact of Task-Switch in Conversational History

EMNLP 2024(2024)

University of Cambridge | CISPA Helmholtz Center for Information Security

Cited 5|Views38
Abstract
With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications. When prompted by users, these AI systems successfully perform a wide range of tasks as part of a conversation. To provide some sort of memory and context, such approaches typically condition their output on the entire conversational history. Although this sensitivity to the conversational history can often lead to improved performance on subsequent tasks, we find that performance can in fact also be negatively impacted, if there is a task-switch. To the best of our knowledge, our work makes the first attempt to formalize the study of such vulnerabilities and interference of tasks in conversational LLMs caused by task-switches in the conversational history. Our experiments across 5 datasets with 15 task switches using popular LLMs reveal that many of the task-switches can lead to significant performance degradation.
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Spoken Dialogue Systems,Multimodal Interaction,Reinforcement Learning,Dialog Management,Semantic Processing
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要点】:本研究首次探讨了对话历史中任务切换对大型语言模型(LLM)任务干扰的影响,发现任务切换可能导致性能显著下降。

方法】:通过在对话历史中引入任务切换,分析LLM在不同任务间的表现变化。

实验】:在5个数据集上进行了15种任务切换实验,使用流行的LLM模型,结果表明任务切换常常会导致性能显著退化。