Recent advancement in code understanding and generation demonstrates that code LLMs fine-tuned on a high-quality instruction dataset can gain powerful capabilities to address wide-ranging code-related tasks. However, most previous existing methods mainly view each programming language in isolation and ignore the knowledge transfer among different programming languages. To bridge the gap among different programming languages, we introduce a novel multi-agent collaboration framework to enhance multilingual instruction tuning for code LLMs, where multiple language-specific intelligent agent components with generation memory work together to transfer knowledge from one language to another efficiently and effectively. Specifically, we first generate the language-specific instruction data from the code snippets and then provide the generated data as the seed data for language-specific agents. Multiple language-specific agents discuss and collaborate to formulate a new instruction and its corresponding solution (A new programming language or existing programming language), To further encourage the cross-lingual transfer, each agent stores its generation history as memory and then summarizes its merits and faults. Finally, the high-quality multilingual instruction data is used to encourage knowledge transfer among different programming languages to train Qwen2.5-xCoder. Experimental results on multilingual programming benchmarks demonstrate the superior performance of Qwen2.5-xCoder in sharing common knowledge, highlighting its potential to reduce the cross-lingual gap.

Expert finding could help route questions to potential suitable users to answer in Community Question Answering (CQA) platforms. Hence it is essential to learn accurate representations of experts and questions according to the question text articles. Recently the pre-training and fine-tuning paradigms are powerful for natural language understanding, which has the potential for better question modeling and expert finding. Inspired by this, we propose a CQA-domain Contrastive Pre-training framework for Expert Finding, named CPEF, which could learn more comprehensive question representations. Specifically, considering that there is semantic complementation between question titles and bodies, during the domain pre-training phase, we propose a title-body contrastive learning task to enhance question representations, which directly treats the question title and the corresponding body as positive samples of each other, instead of designing extra data-augmentation strategies. Furthermore, a personalized tuning network is proposed to inject the personalized preferences of different experts during the fine-tuning phase. Extensive experimental results on six real-world datasets demonstrate that our method could achieve superior performance for expert finding.

Expert Finding is an important task in Community Question Answering (CQA) platforms, which could help route questions to potential users to answer. The key is to learn representations of experts based on their historical answered questions accurately. In this paper, inspired by the strong text understanding ability of Pretrained Language modelings (PLMs), we propose a pre-training and fine-tuning expert finding framework. The core is that we design an expert-level pre-training paradigm, that effectively integrates expert interest and expertise simultaneously. Specifically different from the typical corpus-level pre-training, we treat each expert as the basic pre-training unit including all the historical answered question titles of the expert, which could fully indicate the expert interests for questions. Besides, we integrate the vote score information along with each answer of the expert into the pre-training phrase to model the expert ability explicitly. Finally, we propose a novel reputation-augmented Masked Language Model (MLM) pre-training strategy to capture the expert reputation information. In this way, our method could learn expert representation comprehensively, which then will be adopted and fine-tuned in the down-streaming expert-finding task. Extensive experimental results on six real-world CQA datasets demonstrate the effectiveness of our method.