Text generation has become a critical component in modern AI applications such as chatbots and agentic assistants. To combat hallucinations and provide trustworthy output, retrieval augmented generation (RAG) is the current norm for information-dense tasks. Report Generation is a long-form RAG task with strict attestation requirements that makes it well-suited to explore questions of RAG evaluation and multilingual generation. In this task, a long-form report summarizing the relevant information in a corpus is generated in response to a report request, which consists of a user background and an information need. The generated report should provide proper attribution to the source documents to establish trust.
RAG4Reports would like to draw the attention of the ACL community to developing systems and evaluation methods for the Multilingual Report Generation, which can lead to more general solutions for other long-form RAG problems as well.
CALL FOR PAPERS
RAG4Reports is interested in various aspects of the Report Generation problem, including:
- Report generation and long-form RAG systems
- Multilingual report generation or RAG datasets
- Analysis of various components in report generation systems, such as multilingual retrieval, multilingual generative models.
- Evaluation method for report generation
- Submission Guidelines and Review Process
Papers can either be directly submitted to the RAG4Reports through OpenReview or committed through ARR. Submissions should use the ACL format with at most 8 pages excluding references. Reviewers will be asked to consider the paper’s length when writing their reviews.
IMPORTANT DATES
- Paper submission deadline: March 5, 2026
- Pre-reviewed ARR commitment deadline: March 24, 2026
- Notification of acceptance: April 28, 2026
- Camera-ready paper due: May 12, 2026
- Workshop date: July 2 or 3, 2026 (TBA)