CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation

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.

Retrieval Augmented Cross-Modal Tag Recommendation in Software Q&A Sites

Posts in software Q\&A sites often consist of three main parts: title, description and code, which are interconnected and jointly describe the question. Existing tag recommendation methods often treat different modalities as a whole or inadequately consider the interaction between different modalities. Additionally, they focus on extracting information directly from the post itself, neglecting the information from external knowledge sources. Therefore, we propose a Retrieval Augmented Cross-Modal (RACM) Tag Recommendation Model in Software Q\&A Sites. Specifically, we first use the input post as a query and enhance the representation of different modalities by retrieving information from external knowledge sources. For the retrieval-augmented representations, we employ a cross-modal context-aware attention to leverage the main modality description for targeted feature extraction across the submodalities title and code. In the fusion process, a gate mechanism is employed to achieve fine-grained feature selection, controlling the amount of information extracted from the submodalities. Finally, the fused information is used for tag recommendation. Experimental results on three real-world datasets demonstrate that our model outperforms the state-of-the-art counterparts.

Sequential Tag Recommendation

With the development of Internet technology and the expansion of social networks, online platforms have become an important way for people to obtain information. The introduction of tags facilitates information categorization and retrieval. Meanwhile, the development of tag recommendation systems not only enables users to input tags more efficiently, but also improves the quality of tags. However, current tag recommendation methods only consider the content of the current post and do not take into account the influence of user preferences. Since the main body of tag recommendation is the user, it is very necessary to obtain the user's tagging habits. Therefore, this paper proposes a tag recommendation algorithm (MLP4STR) based on the dynamic preference of user's behavioral sequence, which models the user's historical post information and historical tag information to obtain the user's dynamic interest changes. A pure MLP structure across feature dimensions is used in sequence modeling to model the interaction between tag content and post content to fully extract the user's interests. Finally tag recommendation is performed.