Investigating Task Arithmetic for Zero-Shot Information Retrieval

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 atthis https URL.

@article{braga2025_2505.00649,
  title={ Investigating Task Arithmetic for Zero-Shot Information Retrieval },
  author={ Marco Braga and Pranav Kasela and Alessandro Raganato and Gabriella Pasi },
  journal={arXiv preprint arXiv:2505.00649},
  year={ 2025 }
}