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 - Trustworthiness in Retrieval-Augmented Generation Systems: A Survey ([paper](https://arxiv.org/abs/2409.10102), 2024)
   - Highlight: In this survey, the authors propose **a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy**. Within this framework, the authors thoroughly review the existing literature on each dimension. Additionally, the authors create the evaluation benchmark regarding the six dimensions and conduct comprehensive evaluations for a variety of proprietary and open-source models. Finally, the authors identify the potential challenges for future research based on our investigation results. Through this work, the authors aim to lay a structured foundation for future investigations and provide practical insights for enhancing the trustworthiness of RAG systems in real-world applications.
     
+- A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence ([paper](https://arxiv.org/pdf/2310.05388)
+  - Highlight: This paper presents GROVE, a retrieval-augmented story generation framework designed to help large language models (LLMs) generate complex and credible narratives. The framework consists of three stages: (1) building a retrieval repository of human-written stories based on target control conditions (e.g., plot, mood, genre, subject); (2) constructing an evidence forest through an iterative “asking-why” prompting process to uncover missing or ambiguous background details; and (3) story rewriting by incorporating selected evidence chains to enrich and refine the story. Experimental results show that GROVE outperforms baselines such as ICL, CoT, and Story-S on both human and automatic evaluations, especially in plot complexity and creativity. The approach demonstrates generalizability even on smaller models like Alpaca-Plus-7B, and includes novel metrics and a detailed ablation study to support its findings.
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 ## <a name="ec"></a>[Evaluation Campaigns]()
  
 - **TREC RAG Track** ([site](https://trec-rag.github.io), 2024, 2025)
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 - **BREV-RAG (Beyond Relevance-based EValuation of RAG systems)**
   - Highlight: The workshop of [BREV-RAG@SIGIR-AP 2025](http://sakailab.com/brev-rag/) (**calling for papers now**) focuses on the viewpoint of evaluation, which will be held in December, 2025. 
- 
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 ## <a name="ps"></a>[Papers]()
 
   - ### <a name='ro'></a>[Retrieval Orchestration]()
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     - [XQC25] [CiteEval: Principle-Driven Citation Evaluation for Source Attribution](https://aclanthology.org/2025.acl-long.1574/). ACL.
 
   - ### <a name='mulrag'></a>[Multimodal RAG]()
-    - [AZD25] [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://aclanthology.org/2025.findings-acl.861/). ACL.
+    - [AZD25] [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://aclanthology.org/2025.findings-acl.861/). ACL.
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