Author Response to Ethics Reviewer VuFm
We thank Reviewer K2Un for requesting an ethics review regarding "data privacy, copyright, and consent" for our submission, and further thank Ethics Reviewer VuFm for their valuable perspective and recommendations regarding such ethical considerations. We appreciate the importance of the points you raise and will address them in detail in our revised manuscript.
---
> **Q1: Data Privacy, Security, Copyright, and Consent**
We will expand our section explaining our data collection process, focusing on how we ensure compliance with data protection regulations, maintain privacy, respect copyright, and address consent issues.
Our datasets come from two sources: existing datasets that we modify and/or use, and new data scraped from the web via our data engine.
1. **Existing datasets:** We only use datasets with licenses that explicitly permit usage and repackaging for research purposes. We currently list all datasets used in Appx. E1, but will add information on their licenses, copyright information, and how we maintain compliance with each dataset’s terms.
2. **Data engine:** Our web agent collects data solely from Wikipedia, which is licensed under CC BY-SA (https://en.wikipedia.org/wiki/Wikipedia:Copyrights). We will add details on how we adhere to this license.
3. **Consent considerations:**
- Existing datasets: We will review and report on any consent procedures used by the datasets we incorporate, acknowledging any limitations or concerns.
- Wikipedia data: We will mention the implicit consent given by Wikipedia contributors under the site's terms of service.
> **Q2: Bias and Fairness**
We appreciate the importance of addressing potential biases related to demographic variables. While comprehensive mitigation of bias in MLLMs is not the objective of our work—and, in our opinion, is challenging enough to warrant a standalone work in itself—we recognize its significance. We will include a section including:
1. An analysis of demographic variables in our Cambrian-10M dataset, highlighting any observed patterns or imbalances.
2. A discussion of how training a model on data with inherent biases perpetuates them, as well as challenges and potential unintended consequences of artificially modifying data distributions to address said biases.
3. Acknowledgment that the thorough mitigation of bias in large-scale MLLMs is a complex issue worthy of dedicated research, and we hope our analysis will spur future work focused on this critical issue.
> **Q3: Transparency and Interpretability of the Model’s decision-making processes**
We appreciate the reviewer's suggestion around model interpretability. However, it's important to note that **understanding the decision-making processes of LLMs / MLLMs is a challenging research problem in itself, with only very preliminary work in this direction.** Recent work [1][2][3] has made initial progress toward the mechanistic interpretability of LLMs, but such methods are not yet applicable to open-source models or MLLMs.
While full interpretability of a MLLM's decision-making process is beyond the current state-of-the-art, we can provide some insights into the workings of our SVA module. Specifically, as described in our rebuttal to Reviewer ED7N, we will include an analysis of the attention scores across different vision encoders in our SVA module for various types of images and tasks. This analysis will shed light on how different vision models contribute to the overall visual understanding in different scenarios.
> **Q4: Potential Misuse and Mitigation Strategies**
We appreciate the reviewer's concern about potential misuse of our technology.
However, we must clarify a fundamental misunderstanding: **MLLMs like Cambrian-1 are not capable of generating deepfakes.** They take text + images as input and produce text as output. They cannot generate, manipulate, or synthesize images or videos, as deepfakes entail.
That said, we acknowledge that MLLMs can potentially be misused in other ways, and we take these concerns seriously. We will add discussion of potential risks and mitigation strategies, including:
1. **Misinformation:** MLLMs could be used to generate misleading text descriptions of images or to craft false narratives about visual content. To mitigate this, we will provide guidelines for responsible use of our model.
2. **Hallucination:** Like LLMs, MLLMs might generate outputs not grounded in facts or input data. We will clearly communicate this limitation to users.
---
Thank you again for your valuable feedback on the ethical considerations of our work. Please let us know if you have any other questions or suggestions.
### References
- [1] Bricken et al. Towards monosemanticity: Decomposing language models with dictionary learning. 2023.
- [2] Templeton et al. Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet. 2024.
- [3] Lieberum et al. Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2. 2024.