Investigating Task Arithmetic for Zero-Shot Information Retrieval

• URL: http://arxiv.org/abs/2505.00649v1
• Date: Thu, 01 May 2025 16:48:37 GMT
• Title: Investigating Task Arithmetic for Zero-Shot Information Retrieval
• Authors: Marco Braga, Pranav Kasela, Alessandro Raganato, Gabriella Pasi,
• Abstract summary: Task Arithmetic is a technique that combines the weights of Large Language Models pre-trained on different tasks or domains via simple mathematical operations.<n>Our method is able to synthesize diverse tasks and domain knowledge into a single model, enabling effective zero-shot adaptation in different retrieval contexts.
• Score: 47.300506002171275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown impressive zero-shot performance across a variety of Natural Language Processing tasks, including document re-ranking. However, their effectiveness degrades on unseen tasks and domains, largely due to shifts in vocabulary and word distributions. In this paper, we investigate Task Arithmetic, a technique that combines the weights of LLMs pre-trained on different tasks or domains via simple mathematical operations, such as addition or subtraction, to adapt retrieval models without requiring additional fine-tuning. Our method is able to synthesize diverse tasks and domain knowledge into a single model, enabling effective zero-shot adaptation in different retrieval contexts. Extensive experiments on publicly available scientific, biomedical, and multilingual datasets show that our method improves state-of-the-art re-ranking performance by up to 18% in NDCG@10 and 15% in P@10. In addition to these empirical gains, our analysis provides insights into the strengths and limitations of Task Arithmetic as a practical strategy for zero-shot learning and model adaptation. We make our code publicly available at https://github.com/DetectiveMB/Task-Arithmetic-for-ZS-IR.

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