AI Paper Summaries #78 - LLM Privacy Protection!
10/06/2023
Can Language Models be Instructed to Protect Personal Information?
“Can Language Models be Instructed to Protect Personal Information?” by Yang Chen et al. introduces a novel approach to privacy protection in Large Language Models (LLMs). The authors present PrivQA, a multimodal benchmark designed to assess the privacy/utility trade-off when an LLM is instructed to protect specific categories of personal information in a simulated scenario.
The paper highlights the paradox of LLMs memorizing and leaking pre-training data, which raises serious user privacy and information security concerns. To address this, the authors propose a technique to iteratively self-moderate responses, which significantly improves privacy. However, they also find that adversaries can easily circumvent these protections with simple jailbreaking methods through textual and/or image inputs.
The authors introduce a new dataset of 160k QA pairs derived from 10k driving scenarios, paired with high-quality control commands collected with an RL agent and question-answer pairs generated by a teacher LLM (GPT-3.5). This dataset is used to train the proposed models, demonstrating the potential of LLM-based driving action generation in comparison to traditional behavioral cloning.
In terms of comparison with the state of the art, this paper stands out by critically examining the self-correction capabilities of LLMs. While many studies have focused on improving LLMs’ performance, this work takes a step back to question one of the fundamental assumptions about these models’ abilities.
The authors suggest that their findings offer insights for future research and practical applications in this field. However, they do not specify particular future steps or research directions. The paper does not provide specific quantitative results but instead offers a qualitative analysis of LLMs’ self-correction capabilities.
In conclusion, this paper provides a critical examination of the self-correction capabilities of Large Language Models. It challenges the assumption that these models can effectively correct their own mistakes without external feedback. These insights could have significant implications for the development and application of LLMs in various fields.
Check out the full paper here: https://arxiv.org/pdf/2310.02224.pdf.
Drag View: Generalizable Novel View Synthesis with Unposed Imagery
“Drag View: Generalizable Novel View Synthesis with Unposed Imagery” by Zhiwen Fan et al. introduces a novel and interactive framework for generating novel views of unseen scenes. The authors present DragView, a system that initializes the new view from a single source image, and the rendering is supported by a sparse set of unposed multi-view images.
The paper highlights the paradox of Large Language Models (LLMs) memorizing and leaking pre-training data, which raises serious user privacy and information security concerns. To address this, the authors propose a technique to iteratively self-moderate responses, which significantly improves privacy. However, they also find that adversaries can easily circumvent these protections with simple jailbreaking methods through textual and/or image inputs.
The authors introduce a new dataset of 160k QA pairs derived from 10k driving scenarios. This dataset is used to train the proposed models, demonstrating the potential of LLM-based driving action generation in comparison to traditional behavioral cloning.
In terms of comparison with the state of the art, this paper stands out by critically examining the self-correction capabilities of LLMs. While many studies have focused on improving LLMs’ performance, this work takes a step back to question one of the fundamental assumptions about these models’ abilities.
The authors suggest that their findings offer insights for future research and practical applications in this field. However, they do not specify particular future steps or research directions. The paper does not provide specific quantitative results but instead offers a qualitative analysis of LLMs’ self-correction capabilities.
In conclusion, this paper provides a critical examination of the self-correction capabilities of Large Language Models. It challenges the assumption that these models can effectively correct their own mistakes without external feedback. These insights could have significant implications for the development and application of LLMs in various fields.
Check out the full paper here: https://arxiv.org/pdf/2310.03704v1.pdf.
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
“Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning” by Alexey Skrynnik et al. introduces a novel approach to the Multi-agent Pathfinding (MAPF) problem. The authors present a method that integrates two complementary approaches: planning with heuristic search and reinforcement learning through policy optimization.
The paper highlights the paradox of Large Language Models (LLMs) memorizing and leaking pre-training data, which raises serious user privacy and information security concerns. To address this, the authors propose a technique to iteratively self-moderate responses, which significantly improves privacy. However, they also find that adversaries can easily circumvent these protections with simple jailbreaking methods through textual and/or image inputs.
The authors introduce a new dataset of 160k QA pairs derived from 10k driving scenarios. This dataset is used to train the proposed models, demonstrating the potential of LLM-based driving action generation in comparison to traditional behavioral cloning.
In terms of comparison with the state of the art, this paper stands out by critically examining the self-correction capabilities of LLMs. While many studies have focused on improving LLMs’ performance, this work takes a step back to question one of the fundamental assumptions about these models’ abilities.
The authors suggest that their findings offer insights for future research and practical applications in this field. However, they do not specify particular future steps or research directions. The paper does not provide specific quantitative results but instead offers a qualitative analysis of LLMs’ self-correction capabilities.
In conclusion, this paper provides a critical examination of the self-correction capabilities of Large Language Models. It challenges the assumption that these models can effectively correct their own mistakes without external feedback. These insights could have significant implications for the development and application of LLMs in various fields.
Check out the full paper here: https://arxiv.org/pdf/2310.01207v1.pdf.