A Survey of Large Language Models Attribution
Authors: Dongfang Li, Zetian Sun, Xinshuo Hu, zhenyu liu, Ziyang Chen, Xiang Zhao, Baotian Hu, Min Zhang


Reviewer paper summaries: The paper covers a comprehensive review of the attribution mechanism employed by large language models. It starts with a task definition of attribution - to generate and provide evidence in the form of references or citations for the generated output from large language models. Then it   goes deeper to discussion five aspect of attribution systems: 

1. Sources of attribution - the attributions come from pre-training data and stored in the model parameters, and out-of-model knowledge. 
2. Datasets for attribution - 7 datasets with their domain, source, structure, and gradually.
3. Approaches to attribution - direct generated attribution, post-retrieval answering, and post-generation attribution. 
4. Attributions systems - 4 different attribution systems with their features including modeling training, evidence type, citation type, and integration
5. Attribution evaluation - human evaluation, categorization-based evaluation, and quantitative evaluation metrics. 

Finally, the paper discusses attribution error types, limitation of attributions, and challenges for attributions. It finishes with future direction in this area. It is a solid survey paper of attribution, which gives researchers both in the field and out of field an good overview and valuable implication in the refinement of attribution methodologies.
Summary of strengths: * The paper is well-written and easy to read. In each aspect of LLM attribution, it provides detailed explanation of categories and tables to summarize the approaches, systems, metrics. It provides a good overview of this field to researchers both in the field or new to the field. 

* The paper has a detailed introduction to introduce the problem definition and motivation of LLM attribution research. 

* The paper gives complete citations, which helps readers to find references if they are interested in going deeper.

* The paper concludes with future directions for attribution, which could encourage future research directions in this topic.
Summary of weaknesses: * The paper is a pure survey paper and lacks novel proposed approaches to solve challenges and limitations of LLM attribution research. 

* The paper doesn't have empirical experiments to compare different systems, approaches on different metrics.
Comments, suggestions and typos: * Adding quantitative analyses of different approaches on different data sets and metrics could provide more insights of the current status of the research on LLM attributions.
Overall assessment scores: 3.5