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

Published 28 Feb 2024 in cs.CL | (2402.18216v2)

Abstract: With the recent emergence of powerful instruction-tuned LLMs, various helpful conversational 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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Summary

  • The paper formalizes the impact of task-switch by quantifying LLM task-switch sensitivity through systematic empirical evaluations.
  • It evaluates diverse conversational datasets, revealing consistent performance drops in both small- and large-scale models.
  • The study highlights the need for advanced context management to enhance LLM robustness in dynamic multi-task environments.

Impact of Task-Switching in Conversational Histories on LLM Performance

The study titled "LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History" systematically investigates a novel issue regarding the performance of LLMs in scenarios where a task change occurs within a conversation. This analysis is particularly relevant as LLMs, which are increasingly prevalent in conversational artificial intelligence applications, are often designed to generate responses conditioned on the entire conversation history. This design choice, while beneficial for maintaining continuity and providing context-sensitive responses, can also introduce risks of performance degradation following a task-switch.

Key Contributions

The paper provides three primary contributions to the field of conversational AI:

  1. Formalization of Task-Switch Impact: The study introduces a formal framework to evaluate the risk of performance degradation due to task-switches in a conversation. This is achieved by measuring a model's "task-switch sensitivity," which quantifies the impact of preceding chat history on the model's response to a new and different task.
  2. Empirical Evaluation on Multiple Datasets: The study examines task-switch effects using five datasets spanning different tasks, encompassing a total of over 15 distinct task-switch scenarios. It identifies that even advanced models such as GPT-3.5 and GPT-4 exhibit varying degrees of vulnerability to task-switches.
  3. Analysis Across Model Sizes: The research explores task-switch vulnerabilities across models of different sizes, from smaller (7B parameters) models like Llama and Mistral to larger models such as GPT-3.5, highlighting that both model variants show susceptibility to performance drops post-task-switch.

Experimental Insights

The empirical findings indicate that task-switching can indeed lead to significant performance declines. For instance, a switch from a summarization task to a mathematical reasoning task resulted in a marked drop in performance for models of varying scales. Interestingly, the research suggests that the task-switch sensitivity is not strictly correlated with model size; both large and small models were affected, underscoring a gap in robustness that is independent of model scale.

Moreover, the study computes task-switch sensitivity for different combinations of tasks. The results suggest that tasks with high contextual variance or differing information processing requirements, such as those involving abstract algebra and sentiment classification, are particularly prone to causing LLM confusion in the presence of task-switches.

Implications and Future Directions

The implications of this research are twofold. Practically, understanding and mitigating task-switch sensitivity is crucial for improving the dependability of LLM-based conversational agents in real-world multi-task environments. Theoretically, these findings suggest a need for more robust mechanisms for LLMs to dynamically adjust to task changes without compromising response quality. This might involve developing advanced context management algorithms or integrating more sophisticated context-awareness features into existing models.

Future work could focus on devising techniques to enhance LLM resilience to task-switches, potentially through improved training methodologies that simulate task-switch scenarios or through innovations in model architecture that better compartmentalize contextual information.

In summary, this paper highlights an important and underexplored vulnerability in LLMs, offering a foundational framework and preliminary insights that could guide future research aimed at enhancing the robustness and applicability of LLMs in diverse and dynamic conversational settings.

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