Benchmarking Semantic Sensitive Information in LLMs Outputs
Authors: Qingjie Zhang, Di Wang, Tianwei Zhang, Chao Zhang, Han Qiu


Reviewer paper summaries: This paper proposes a new benchmark for evaluating Semantic Sensitive Information (SemSI) in LLM outputs. The authors first define Semantic Sensitive Information, which includes privacy violations, reputational harm, and the spread of misinformation. They propose a dataset, SemSI-Set, with 10,830 prompts and several metrics to study this phenomenon. The authors conduct evaluation and analysis on 22 models, revealing the characteristics of SemSI and providing insights for mitigation solutions.
Summary of strengths: 1. The concept of SemSI is interesting; evaluating LLM's output from other dimensions is valuable.
2. The authors conducted comprehensive experiments, covering many models and providing detailed analysis.
3. The paper is well-written and easy to follow.
Summary of weaknesses: 1. My main concern lies in the definition of SemSI. From the examples in Tables 5, 6, and 7, it is evident that SemSI includes a lot of information related to public persons, places, and institutions. However, different people have different definitions of sensitivity, and this public information may not be sensitive to all people. It is necessary for LLMs to provide essential information to users rather than hiding all information. Additionally, considering misinformation as sensitive information seems somewhat inappropriate, as this issue is usually regarded as a model hallucination.
2. There is a lack of strategies to prevent SemSI. Can this issue be avoided by adding text like "do not include any SemSI" in the prompt?
Comments, suggestions and typos: None
Overall assessment scores: 2.5