A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Augmented Generation (RAG). However, the vanilla approach that simply concatenates multiple pieces of knowledge from different languages into the context may fail to improve effectiveness due to the potential disparities across languages. To better leverage multilingual knowledge, we propose CroSearch-R1, a search-augmented reinforcement learning framework to integrate multilingual knowledge into the Group Relative Policy Optimization (GRPO) process. In particular, the approach adopts a multi-turn retrieval strategy with cross-lingual knowledge integration to dynamically align the knowledge from other languages as supplementary evidence into a unified representation space. Furthermore, we introduce a multilingual rollout mechanism to optimize reasoning transferability across languages. Experimental results demonstrate that our framework effectively leverages cross-lingual complementarity and improves the effectiveness of RAG with multilingual collections.

Causal Inference (CI) plays a crucial role in building unbiased recommender systems. However, most current CI-based debiasing methods only pay attention on either popularity bias or conformity bias. This paper presents a Disentangled Counterfactual Reasoning framework to alleviate dual biases in recommendation, so called DCR. Concretely, we consider the impact of both item popularity and user conformity during training, and separate their indirect effects by disentangling user and item embeddings into biased and unbiased components. In the inference stage, we perform counterfactual reasoning to simultaneously mitigate the indirect and direct effects of bias factors. Experimental results demonstrate the effectiveness of our DCR.

Large language models (LLMs) have been widely applied in recommender systems, achieving remarkable success. However, LLM-based recommendation (LR) suffers from more severe popularity bias than conventional recommendation (CR), stemming from both training and inference stages. In this paper, we propose a novel debiasing method for LR, which performs debiasing in such two stages, so termed as Dual Debiasing in LR (D²LR). Concretely, in the training stage, we conduct token-wise inverse propensity score weighting to force the LLM to pay more attention on unpopular tokens. In the inference stage, we train a more biased CR model by increasing the weights of popular items, which adjusts the generation probability of corresponding tokens according to its scores for items, hoping to suppress the excessive generation of popular tokens. Experiments conducted on three real-world datasets validate the effectiveness of our D²LR in mitigating popularity bias in LR.

In extreme multi-label text classification (XMC), labels usually follow a long-tailed distribution, where most labels only contain a small number of documents and limit the performance of XMC. Data augmentation (DA) is a simple but effective strategy to solve such low-resource problems. In this paper, we propose a prompt-based DA method called XDA, which is specifically designed for XMC. First, we employ a soft prompt during the fine-tuning process of the T5 model for label-conditional DA, thereby enabling T5 to augment samples while preserving label-compatibility. Subsequently, XDA performs sample filtering on the augmented samples through the diversity of text and the consistency of labels, which enhances the quality of the DA. In contrast to traditional sample-level DA, we propose a pair-level DA method by masking the augmented sample-label pairs of head-labels during training, effectively mitigating the long-tailed problem. Comprehensive experiments on benchmark datasets have shown that the proposed XDA outperforms the state-of-the-art counterparts.

Multi-label text classification (MLTC) involves tagging a document with its most relevant subset of labels from a label set. In real applications, labels usually follow a long-tailed distribution, where most labels (called as tail-label) only contain a small number of documents and limit the performance of MLTC. To facilitate this low-resource problem, researchers introduced a simple but effective strategy, data augmentation (DA). However, most existing DA approaches struggle in multi-label settings. The main reason is that the augmented documents for one label may inevitably influence the other co-occurring labels and further exaggerate the long-tailed problem. To mitigate this issue, we propose a new pair-level augmentation framework for MLTC, called Label-Specific Feature Augmentation (LSFA), which merely augments positive feature-label pairs for the tail-labels. LSFA contains two main parts. The first is for label-specific document representation learning in the high-level latent space, the second is for augmenting tail-label features in latent space by transferring the documents second-order statistics (intra-class semantic variations) from head labels to tail labels. At last, we design a new loss function for adjusting classifiers based on augmented datasets. The whole learning procedure can be effectively trained. Comprehensive experiments on benchmark datasets have shown that the proposed LSFA outperforms the state-of-the-art counterparts.