 **Summary:**
The paper introduces Q-Bench, a benchmark designed to evaluate the low-level visual abilities of multi-modal large language models (MLLMs) across three distinct tasks: low-level visual perception, low-level visual description, and overall visual quality assessment. The benchmark includes two datasets: LLVisionQA and LLDescribe, which focus on perception and description abilities, respectively. The authors employ GPT-4V for evaluating these abilities, comparing it against human evaluations. The benchmark aims to provide a comprehensive assessment of MLLMs' capabilities in low-level visual tasks, which are often overlooked in traditional benchmarks. However, the paper is criticized for its limited novelty and the lack of a significant advancement over existing benchmarks.

**Strengths:**
- The paper addresses an important and relevant problem by evaluating the low-level visual abilities of multi-modal large language models (MLLMs), which is a significant gap in current benchmarks.
- The paper is well-written, clear, and easy to follow, making it accessible to a broad audience.
- It introduces a new benchmark, Q-Bench, which is a significant contribution to the field, and includes two datasets: LLVisionQA and LLDescribe, which are well-designed and comprehensive.
- The paper includes a detailed evaluation of MLLMs on low-level visual tasks, providing a valuable resource for the research community.
- The authors have demonstrated the potential of MLLMs in low-level visual tasks, showing that even without specific training, these models can perform well.

**Weaknesses:**
- The paper lacks novelty as it primarily evaluates existing models without proposing new methods or techniques.
- The benchmark's scope is limited to low-level visual abilities, which might not be sufficiently generalizable to other types of visual tasks.
- The paper does not adequately address the bias in the benchmark, particularly the bias towards "good" answers in MLLM outputs, which could affect the reliability of the results.
- The evaluation process is not clearly explained, and there is a lack of detail on how the human-annotated labels were collected and verified, which could affect the validity of the results.
- The paper does not include a significant number of images in the benchmark, which could limit the generalizability of the findings.
- The paper does not discuss the potential impact of the benchmark on the research community, which is a critical aspect of the evaluation.

**Questions:**
- Can the authors clarify how the bias in MLLM outputs was addressed in the evaluation process?
- How were the human-annotated labels collected and verified, and what measures were taken to ensure their accuracy and reliability?
- Could the authors provide more details on the collection and selection process of the images in the benchmark, especially regarding the diversity and representativeness of the images?
- How does the benchmark address the issue of bias towards "good" answers in MLLM outputs, and what steps were taken to mitigate this bias?
- Could the authors discuss the potential impact of the benchmark on the research community and how it might influence future research in the field?
- How does the paper ensure the generalizability of the findings to other types of visual tasks beyond low-level visual abilities?

**Soundness:**
3 good

**Presentation:**
3 good

**Contribution:**
3 good

**Rating:**
6 marginally above the acceptance threshold

**Paper Decision:**
- Decision: Accept
- Reasons: The paper presents a valuable benchmark for evaluating the low-level visual abilities of MLLMs, which is a significant gap in current research. Despite some concerns about the novelty and the limited scope of the benchmark, the paper is well-written, clear, and provides a comprehensive evaluation of MLLMs on low-level visual tasks. The reviewers have highlighted the importance of the benchmark and its potential impact on the field, recommending its acceptance as a poster presentation. The decision aligns with the overall sentiment that the paper contributes valuable insights to the community, even if it does not represent a significant advancement over existing benchmarks.