Yu-An Liu

Yu-An Liu, Ruqing Zhang, Jiafeng Guo et al.

Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ensuring their reliability in practice, has also garnered significant attention. With a wide array of research on robust IR being proposed, we believe it is the opportune moment to consolidate the current status, glean insights from existing methodologies, and lay the groundwork for future development. We view the robustness of IR to be a multifaceted concept, emphasizing its necessity against adversarial attacks, out-of-distribution (OOD) scenarios and performance variance. With a focus on adversarial and OOD robustness, we dissect robustness solutions for dense retrieval models (DRMs) and neural ranking models (NRMs), respectively, recognizing them as pivotal components of the neural IR pipeline. We provide an in-depth discussion of existing methods, datasets, and evaluation metrics, shedding light on challenges and future directions in the era of large language models. To the best of our knowledge, this is the first comprehensive survey on the robustness of neural IR models, and we will also be giving our first tutorial presentation at SIGIR 2024 \url{https://sigir2024-robust-information-retrieval.github.io}. Along with the organization of existing work, we introduce a Benchmark for robust IR (BestIR), a heterogeneous evaluation benchmark for robust neural information retrieval, which is publicly available at \url{https://github.com/Davion-Liu/BestIR}. We hope that this study provides useful clues for future research on the robustness of IR models and helps to develop trustworthy search engines \url{https://github.com/Davion-Liu/Awesome-Robustness-in-Information-Retrieval}.

Wenda Wei, Yu-An Liu, Ruqing Zhang et al.

Retrieval-augmented generation (RAG) has proven to be effective in mitigating hallucinations in large language models, yet its effectiveness remains limited in complex, multi-step reasoning scenarios. Recent efforts have incorporated search-based interactions into RAG, enabling iterative reasoning with real-time retrieval. Most approaches rely on outcome-based supervision, offering no explicit guidance for intermediate steps. This often leads to reward hacking and degraded response quality. We propose Bi-RAR, a novel retrieval-augmented reasoning framework that evaluates each intermediate step jointly in both forward and backward directions. To assess the information completeness of each step, we introduce a bidirectional information distance grounded in Kolmogorov complexity, approximated via language model generation probabilities. This quantification measures both how far the current reasoning is from the answer and how well it addresses the question. To optimize reasoning under these bidirectional signals, we adopt a multi-objective reinforcement learning framework with a cascading reward structure that emphasizes early trajectory alignment. Empirical results on seven question answering benchmarks demonstrate that Bi-RAR surpasses previous methods and enables efficient interaction and reasoning with the search engine during training and inference.

Yu-An Liu, Ruqing Zhang, Mingkun Zhang et al.

Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations to legitimate documents. This vulnerability raises significant concerns about their reliability and hinders the widespread deployment of NRMs. By incorporating adversarial examples into training data, adversarial training has become the de facto defense approach to adversarial attacks against NRMs. However, this defense mechanism is subject to a trade-off between effectiveness and adversarial robustness. In this study, we establish theoretical guarantees regarding the effectiveness-robustness trade-off in NRMs. We decompose the robust ranking error into two components, i.e., a natural ranking error for effectiveness evaluation and a boundary ranking error for assessing adversarial robustness. Then, we define the perturbation invariance of a ranking model and prove it to be a differentiable upper bound on the boundary ranking error for attainable computation. Informed by our theoretical analysis, we design a novel \emph{perturbation-invariant adversarial training} (PIAT) method for ranking models to achieve a better effectiveness-robustness trade-off. We design a regularized surrogate loss, in which one term encourages the effectiveness to be maximized while the regularization term encourages the output to be smooth, so as to improve adversarial robustness. Experimental results on several ranking models demonstrate the superiority of PITA compared to existing adversarial defenses.