Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis.
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Summary
The paper proposes an LLM-based personal activity generation by utilizing existing OpenAI LLM through API, named as LLMob. The problem that authors are time and important research topic in the area of human mobility simulation. While existing generative solely trained on the given dataset, LLM agent has already trained internet-wide dataset that allows it to have possibility of generating human-like trajectories. The LLMob generates activity trajectories in two stages: the first stage identifies (learns) activity patterns with respect to different personas introduced by the authors, and in the second stage a synthetic version of the activity sequences generated by LLM agent. The experiments are performed on an activity dataset, and performance is benchmarked by multiple generative models.
Strengths
The paper is a good example of an agentic-LLM, showcasing the powerful capabilities of LLMs in human mobility modeling. This application is significant as it uses LLMs' data processing and predictive abilities to improve our understanding of human movement patterns, impacting urban planning, transportation, and public health. The paper is well-structured, with a clear and coherent flow, and features a promising presentation that enhances comprehension.
Weaknesses
These are the set of weaknesses of the paper from my perspective: 1-) The authors performed experiments on one dataset. It might be interesting to examine the performance with dataset from a different region. 2-) The model solely relies on the pre-trained LLM models. While the LLM capabilities and power are increasing, which is good for this work, separate fine-tuning might be required for concrete human mobility modeling. 3-) The motivation retrieval part is not clear, especially the learning-based motivation retrieval.
Questions
While Phase 1 and Phase 2 make sense. I have concerns about how much information was carried from Phase 1 to Phase 2. The following are the questions that can help me to understand the details: 1-) Did you utilize the whole dataset in Phase 1? 2-) How are the trajectories and personas sampled in Phase 2? 3-) How do the generated activities match with the input trajectories? Does LLM memorize exact activity sequences? 4-) LLMs are known for hallucination. Within generated activities, the LLM agent generates an activity name and location. How accurate is the LLM agent for the generated activities? For instance, when an activity says Japanese Restaurant (35.369, 140.306), does the GPS location have a Japanese Restaurant? I expect the generated trajectories to require extensive post-processing. 5-) Have you tried your model on a different dataset? Experiments are limited to one dataset, which limits generalizability.
Rating
6
Confidence
4
Soundness
3
Presentation
4
Contribution
3
Limitations
I found two limitations in this paper and would love to hear authors responses on these: 1-) While LLMs are generally powerful, they often struggle to generalize in domain-specific applications. For a robust human mobility simulation, LLM agents might require a fine-tuning strategy to achieve more generalizable performance. 2-) The generated locations in the study are limited to those specified in the prompt (If they do not hallucinate). Since the model also takes into account the locations a person is likely to visit, as illustrated in Figure 2, the degree of synthetic realism in the generated trajectories raises questions. 3-) This approach also does not fully address privacy concerns associated with mobility trajectories as mentioned in the introduction, the underlying patterns and personal data might still be exposed.
Rebuttal by Authors
> **Limitation 1** *While LLMs are generally powerful, they often struggle to generalize in domain-specific applications. For a robust human mobility simulation, LLM agents might require a fine-tuning strategy to achieve more generalizable performance.* **Answer**: We agree that fine-tuning the LLM for a specific domain may deliver better results. However, this needs careful design of instruction-tuning data, and the tuning may incur significant training costs, considering that we are using the GPT API. Indeed, we have tried fine-tuning GPT-3.5. After comparison, we chose the current approach for a better trade-off. > **Limitation 2** *The generated locations in the study are limited to those specified in the prompt (If they do not hallucinate). Since the model also takes into account the locations a person is likely to visit, as illustrated in Figure 2, the degree of synthetic realism in the generated trajectories raises questions.* **Answer**: We appreciate the reviewer's observation. Our approach to specifying likely locations is grounded in research by [1], which shows that individuals typically have characteristic geographical ranges for daily activities. [1] Alessandretti et al. The scales of human mobility. Nature, 2020, 587(7834): 402-407. However, our method does not limit generated locations to those specified. The LLM can reason beyond these locations, potentially introducing new, plausible ones based on the context and motivation. This balances realistic patterns with diversity. The use of location priors enhances generation efficiency and grounds outputs in realistic spatial contexts. Our evaluations, comparing generated trajectories to real-world data, demonstrate that our method produces patterns closely matching real-world data across various metrics. In future work, we plan to explore dynamically expanding the set of potential locations based on evolving trajectory contexts, further enhancing realism and flexibility. > **Limitation 3** *This approach also does not fully address privacy concerns associated with mobility trajectories as mentioned in the introduction, the underlying patterns and personal data might still be exposed.* **Answer:** We suppose that the reviewer's concern comes from the data exposed to the OpenAI. To address this concern, we conducted experiments using Llama 3-8B on a local GPU to ensure data security. The results are reported as follows (we also tested GPT-4o-mini): | Model | SD | SI | DARD | STVD | |----------------|----------|-----------|--------|--| | LLMob-L (GPT-3.5-turbo) | 0.049 | 0.054 | 0.126 | 0.570 | | LLMob-L (GPT-4o-mini) | 0.049 | 0.055 | 0.141 | 0.577 | |LLMob-L (Llama 3-8B) | 0.054 | 0.063 | 0.119 | 0.566 | | LLMob-E (GPT-3.5-turbo) | 0.053 | 0.046 | 0.125 | 0.559 | | LLMob-E (GPT-4o-mini) | 0.041 | 0.053 | 0.211 | 0.531 | | LLMob-E (Llama 3-8B) | 0.054 | 0.059 | 0.122 | 0.561 | We observed competitive performance of our framework when other LLMs were used. In particular, Llama 3-8B is overall the best when spatial and temporal factors are evaluated together (DARD and STVD). Such results demonstrate the robustness of our framework across different LLMs. On the downside, there are a few cases when Llama 3-8B generated locations that do not exist. Such hallucination was not observed on GPT-3.5-turbo and GPT-4o-mini. Nonetheless, we expect that the promising results of this open LLM can inspire further studies, particularly in developing better fine-tuning techniques for human mobility modeling.
I would like to thank the authors for their detailed rebuttal. However, I will be maintaining my original score.
Re: Official Comment by Reviewer c5UM
Thank you very much for kindly taking the time to respond to our rebuttal. We greatly appreciate your valuable comments and suggestions. They will be reflected in the revised paper.
Summary
This paper proposes an LLM-based agent framework for generating personal mobility. It tries to address several problems in the domain of personal mobility generation, including aligning LLMs with the urban data in the real world, developing strategies with reliable activities, and exploring further applications in this field. For generating activities of personal mobility (i.e., trajectories), it proposes a framework of LLM-based agent with an action module, memory module, and planning module, which of all these modules can improve the alignment of the results of generation and real-world data. The paper provides several experiments to verify the effectiveness and alignment of the proposed methods and explores further enhancement.
Strengths
1. This paper is the first simulation with LLM-based agents to generate personal mobility, which can contribute to many fields, such as recommender systems and human behavior studies. 2. The proposed pipeline is intuitive, where the action, memory, and planning modules are important for improving the simulation alignment. 3. The motivation is practical, and the experiments are abundant as well.
Weaknesses
1. What about other LLMs performing as the core, besides GPT-3.5-turbo? I think GPT-4/GPT-4o can be utilized to generate data of higher quality. 2. For the method of Learning-based Motivation Retrieval, I'm a bit concerned about the calculation of similarity among dates. Although the locations are greatly important to identify, there seems to be too much information lost from their initial motivations. Could you try just using cosine similarities on the semantic embeddings of contents with language models? Or maybe using LLMs with prompting to get the score of similarity? 3. I think the presentation can be improved. A preliminary can be added, and the overview of LLMob can be more clear at the start of section 3.
Questions
Please see the above "Weakness". I'm willing to improve my rating if the authors can address my concerns.
Rating
5
Confidence
3
Soundness
3
Presentation
2
Contribution
3
Limitations
The authors have discussed the limitations in the last section.
Summary
This paper introduces LLMob, a framework for personal mobility generation using a large language model. The framework aims to leverage urban activity patterns for emulating urban residents, facilitating the human mobility trajectory generation. Using the Tokyo personal activity dataset, the effectiveness of the proposed framework are validated.
Strengths
- The authors proposed LLMob, an interesting human mobility generation framework with Large Language Models. - The authors provide an illustrative analysis of how LLM can generate reliable activity strategies.
Weaknesses
- The proposed framework has only been validated in GPT-3.5. It remains unclear whether the performance would vary with different backbone models, such as Llama-2. - The constructed Tokyo dataset statistic is not very clear in this paper, which makes it difficult for the reader to evaluate the true contribution of this paper. - The difference between this work and existing work (such as [1]) is not well-discussed in the related work section, although the author claims why these methods cannot be easily adopted as the baselines. [1] Shao, Chenyang, et al. "Beyond Imitation: Generating Human Mobility from Context-aware Reasoning with Large Language Models." arXiv preprint arXiv:2402.09836 (2024).
Questions
- In Line 250 - Line 253, 100 users are chosen and 10 candidate personas are used for subsequent pattern generation. Why did the author select such a setting, I don't see a clear illustration of these settings in the paper. - In Line 46 and Line 45, the author mentioned "unseen tasks" and "unseen scenarios". In my perspective, the personal mobility generation task is well-formulated in this paper, what does "unseen" mean here for those data-driven methods?
Rating
4
Confidence
4
Soundness
2
Presentation
2
Contribution
2
Limitations
In this paper, all experiments are conducted on a single collected dataset (perhaps thousands of trajectories) from Tokyo, which makes this paper not comprehensive enough. Extending the proposed method to more cities helps improve paper quality.
Response to new comments from reviewer xLxx
Thanks for acknowledging our response and comments. We would like to highlight our arguments: 1. Regarding Weakness 3, from the review comments, we did not find the reviewer requiring an explanation on whether previous methods can be easily adopted as baselines. In addition, for the paper mentioned by the reviewer, its source code and prompt were **not available** (the repo was expired when we accessed it, and the paper did not mention what LLM was used) at the time of our submission to NeurIPS. 2. We are sorry that the reviewer is still confused with Question 1. 3. For Question 2, our claim in the introduction is not about the experimental setting. It is essentially a claim on the advantages of using LLMs in real-world applications.
Response to The Author
- **About weakness 3: the difference between this work and existing work** Although the authors claim the difference in their framework (e.g., it is a data-driven framework and features a consistency evaluation scheme), they didn't explain the advantage behind this. The difference means for what aspect makes this work different from existing work and what its potential contribution is, instead of just explain the difference in module-desgin. Unfortunately, this suggestion appears to have been disregarded by the authors. - **About question 1** What I want to know is the motivation or reason behind this setting, e.g., if 100 users and 10 candidates is sufficiently representative of this problem. The author should present this but they didn't. - **About question 2: "unseen" mean here for those data-driven methods?** What I want to know is the definition of "unseen" for previous data-driven methods. It seems that the author explains the "unseen" for LLMs and ignores this. Is there a possibility that several traditional methods can deal with "unseen" tasks? Overall, I think the current explanation is not well-illustrative. - **About limitations on experimental setting** We the authors demonstrate the proposed framework on Osaka, but the validation part is still not comprehensive enough due to limited sample sizes (only thousands of trajectories here in Tokyo). Regarding the supplementary experiments on GPT-3.5, GPT-4, and Llama 3-8B, the author seems to not explain which setting they use. Is it conducted on the (Normal Trajectory, Normal Data) setting? - **Other weakness** 1. The latency and the cost of invoking gpt3.5 API is not reported in this paper, which makes the efficiency of such an LLM-based framework remain a potential issue. 2. The details of dataset construction (section 4.1) are still not clear. For an LLM-based application, it is very important to clearly present these details, and this will make it easy for other researchers to follow.
Response to new comments from reviewer xLxx (1/2)
Thanks for the constructive discussion. We would like to respond as follows: > **Weakness 3**: *Although the authors claim the difference in their framework (e.g., it is a data-driven framework and features a consistency evaluation scheme), they didn't explain the advantage behind this. The difference means for what aspect makes this work different from existing work and what its potential contribution is, instead of just explain the difference in module-desgin. Unfortunately, this suggestion appears to have been disregarded by the authors.* In the previous response, we summarized two significant differences compared to the work mentioned by the reviewer (MobiGeaR): **data-driven** framework and **data alignment** scheme. 1. A data-driven model has the following advantage: it can continuously learn and improve from new data. As more data becomes available, the model's performance can be enhanced, making it adaptive to evolving patterns and trends. Specifically, we demonstrated that our model can automatically adjust to changes in mobility patterns caused by events like the pandemic. In contrast, the mechanistic gravity model used in MobiGeaR is an analytical model, which does not have the capability of learning from and adapting to data. This difference becomes crucial in real-world applications where urban mobility patterns are constantly changing due to factors like urban development, policy changes, or societal shifts. 2. The data alignment scheme has the advantage of ensuring the self-consistency of the identified pattern. The importance of ensuring self-consistency in using LLMs has been investigated in previous studies such as [1]. We did not find such a self-consistency mechanism in MobiGeaR. This feature is of importance for the fidelity of simulated trajectories to actual human behavior is paramount. [1] Wang X, Wei J, Schuurmans D, et al. Self-consistency improves chain of thought reasoning in language models[J]. arXiv preprint arXiv:2203.11171, 2022. To reflect the reviewer's concern, we would like to include a discussion on the differences from MobiGeaR in the next version of the paper. > **Question 1**: *What I want to know is the motivation or reason behind this setting, e.g., if 100 users and 10 candidates is sufficiently representative of this problem. The author should present this but they didn't.* Increasing the number of users and candidate personas improves the sufficiency and diversity of data. On the downside, this compromises the efficiency. We choose such numbers to balance between the two factors. We found that the choice of 100 users and 10 candidate personas is good enough for promising results. In the next version, we would like to include the above explanation. > **Question 2**: *What I want to know is the definition of "unseen" for previous data-driven methods. It seems that the author explains the "unseen" for LLMs and ignores this. Is there a possibility that several traditional methods can deal with "unseen" tasks? Overall, I think the current explanation is not well-illustrative.* By the meaning of "unseen", we meant that the data or scenarios are unseen to the model. For traditional mobility generation methods, we do not think they can handle unseen tasks, because "unseen" refers to data points or patterns that fall outside the distribution of the training data. These methods often struggle with extrapolation to out-of-distribution scenarios. In contrast, our LLM-based approach leverages semantic understanding and general knowledge to reason about the scenarios on which it has not been trained. In addition, such adaptation to unseen scenarios is easy in LLMs by using only a prompt. > **Limitation**: *We the authors demonstrate the proposed framework on Osaka, but the validation part is still not comprehensive enough due to limited sample sizes (only thousands of trajectories here in Tokyo). Regarding the supplementary experiments on GPT-3.5, GPT-4, and Llama 3-8B, the author seems to not explain which setting they use. Is it conducted on the (Normal Trajectory, Normal Data) setting?* We used the (Normal Trajectory, Normal Data) setting. Due to the limited time during the rebuttal period, we cannot cover all the settings. Nonetheless, we would like to report a comprehensive evaluation of the Osaka dataset in the next version of the paper.
Response to new comments from reviewer xLxx (2/2)
> **Other Weakness 1**: *The latency of the cost of invoking gpt3.5 API is not reported in this paper, which makes the efficiency of such an LLM-based framework remain a potential issue.* For GPT-3.5 API, the average generation time per trajectory for the results reported in Table 1 is 46 seconds. We would like to report this in the next version of the paper. Moreover, if users seek better efficiency, we suggest using open models such as Llama 3-8B, seeing its promising results as well. > **Other Weakness 2**: *The details of dataset construction (section 4.1) are still not clear. For an LLM-based application, it is very important to clearly present these details, and this will make it easy for other researchers to follow.* For researchers who are interested in our work, we will provide such details in the open-source repo.
Thanks
After carefully reading the author's response, partial concerns were addressed. However, some important parts are still not clear: (1) The dataset construction details, which are very important for the researcher to follow this work; (2) the Lack of statistically rigorous reason behind some settings, e.g., if 100 users and 10 candidates ); (3) The validation size is the obvious limitation of this paper. Hope these comments and suggestions could help the author further improve their paper quality. Best,
Thank you for the comments
Thank you for your time and the valuable comments on this paper. We would like to address your concerns as much as possible: > **Concern 1**: *The dataset construction details, which are very important for the researcher to follow this work.* **Answer:** We agree with the necessity of explaining the dataset construction. In this paper, the data format has been given in Table 2, and dataset statistics has been reported in the attached pdf file in the above "more detailed information about the experimental data". We obtained the dataset through Twitter (now X)'s Academic Research Product Track. The construction of the dataset is reported as follows: 1. **Filtering Incomplete Data**: Users with missing check-ins for a specific year were filtered out. 2. **Excluding Non-Japan Check-ins**: Check-ins that occurred outside of Japan were removed. 3. **Inferring Prefecture from GPS Coordinates**: Prefectures were inferred based on the latitude and longitude data of check-ins. 4. **Assigning Prefecture**: Users were assigned to a prefecture based on their primary check-in location; e.g., users whose top check-in location is Tokyo were categorized as belonging to Tokyo. 5. **Removing Sudden-Move Check-ins**: Check-ins showing abrupt, unrealistic location changes, such as from Tokyo to the Osaka within a very short time frame, were deleted to remove data drift (i.e., fake check-ins), following the criteria proposed by [1]. [1] Yang, D., Zhang, D., & Qu, B. (2016). Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 7(3), 1-23 6. **Anonymizing Data**: Real user IDs and geographic location names were anonymized. Only category information of Geographic location was kept, and latitude and longitude coordinates were converted into IDs before being input into the model. In the next version of the paper, we will cover the construction process in the appendix, including the raw data collection and the preprocessing. We will also provide open data demos and codes related to this research. > **Concern 2**: *the Lack of statistically rigorous reason behind some settings, e.g., if 100 users and 10 candidates* **Answer:** We would like to emphasize that the chosen settings, such as 100 users and 10 candidates, were selected based on practical considerations and the specific context of our study. These settings were chosen to manage a balance between computational efficiency and the ability to demonstrate the effectiveness of our model. The effectiveness has been shown in the experimental section, and the efficiency result has been reported in the above discussion. > **Concern 3**: *The validation size is the obvious limitation of this paper.* **Answer:** We agree that having a larger validation size would be beneficial in demonstrating the model's performance. However, due to constraints including the data availability and computational resources, we had to make practical decisions regarding the validation size in this research. We believe that our chosen setting is reasonable, which is partly supported by the similar evaluation setting taken within the 25-LLM-agent community constructed in [1]. [1] Park J S, O'Brien J, Cai C J, et al. Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th annual ACM symposium on user interface software and technology. 2023: 1-22.
Re: Thanks
Following our previous post, in which we tried to address the most recent concerns of the reviewer, we sincerely appreciate the reviewer's dedicated efforts in evaluating our paper and engaging in the discussion. Throughout the discussions, we have tried our best to address the raised questions, particularly regarding the dataset details (construction & statistics) and the inclusion of additional experiments. We find these discussions highly constructive for improving the quality of our paper. Regarding the identified limitations, we acknowledge that expanding the number of users and candidate personas could potentially enhance the data's sufficiency and diversity. However, we wish to emphasize that our current selection of 100 users and 10 personas has already yielded **promising outcomes**, as demonstrated in our results. It is important to note that increasing these numbers would lead to **higher token consumption and computational costs**. This limitation is not unique to our study but is also **observed in many notable existing works** that utilize GPT 3.5/4 APIs, including the seminal work of generative agents [1], which involved only 25 agents. [1] Park J S, O'Brien J, Cai C J, et al. Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th annual ACM symposium on user interface software and technology. 2023: 1-22. We are committed to addressing all concerns highlighted by the reviewer in the next version of our paper, incorporating all details from this discussion. We hope that these efforts meet the reviewer's expectations and merit consideration for a higher rating.
Summary
This paper presents a prompt engineering framework for generating synthetic human trajectories using LLMs. The framework is guided, in an overall manner, by the observation that human movement is affected by habitual activity patterns and motivations. In this way, the framework has two phases for considering these aspects. In phase 2, two varying strategies are proposed and compared. All in all, this is an interesting piece of application work of LLMs, in prompt engineering.
Strengths
1) A general solid prompt engineering framework for an interesting application in cities. 2) The method is non-trivial and has a certain level of novelty. 3) The evaluation has been comprehensive.
Weaknesses
1) There are some parts in the text that are difficult to decipher – I wouldn’t say the paper is easy to follow. 2) It is unclear which method actually yields the best performance across different metrics 3) The generalizability is unclear. Can this method be used in other cities around the globe?
Questions
1) Is this method zero-shot? 2) How is the Td in 3.2 (habitual activity pattern) used subsequently? 3) What’s the relation between 3.2.1 evolving based motivation retrieval and 3.2.2 learning-based motivation retrieval? 4) Learning-based motivation retrieval appears to be dependent on embeddings obtained through a contrastive learning process. In what way the embeddings are actually used? 5) What’s the rationale behind the two retrieval strategies? If one is superior, why the other is being discussed?
Rating
6
Confidence
4
Soundness
3
Presentation
2
Contribution
2
Limitations
See weaknesses
Response to The Author
Thanks for the responses, which partially demonstrate the effectiveness of the proposed method on other LLM backbones and datasets. However, the author fails to answer if previous methods cannot be easily adopted as the baselines (mentioned in weakness 3), and fails to provide more well-illustrative reasons for several settings in this paper (mentioned in questions 1 and question 2, the author just explains this again, but didn't provide a reasonable explanation). Considering this paper also has limitations on experimental settings, which still need time and effort to refine, I will keep my score, and I hope these comments could help the author further improve their paper quality. Best,
Thanks for the rebuttal by the authors. I would like to raise my score to 5.
Response to Reviewer's Engagement
Thank you very much for kindly taking the time to respond to our rebuttal! We also greatly appreciate your valuable comments and suggestions. They will be reflected in the revised paper.
Thanks
I would like to thank the authors for the response, and I would like to raise my score.
Re: Thanks
Thank you very much for kindly taking the time to respond to our rebuttal! We also greatly appreciate your valuable comments and suggestions. They will be reflected in the revised paper.
Response to ethics review
Thank you for your valuable comments. We fully understand and acknowledge the importance of privacy issues and would like to address your concerns as follows: > **Comment 1**: *The paper uses a dataset "obtained through Twitter and Foursquare APIs and covers the data from January 2019 to December 2022". This is potentially personally identifiable information. It is also not clear whether the location information is collected with the consent of the individuals who have been tracked. Considering that movement patterns are highly individual it is also possible that individuals can be de-identified from such data. Details or a discussion about the privacy concerns of using such a dataset, as well as the measures put in place to reduce such risks are missing. I would recommend that the authors describe further the data and the measures they've taken to ensure the privacy of the individual whose data they are using.* **Answer:** We would like to clarify that all the data used in our study is indeed anonymous. First of all, the data obtained through the Twitter and Foursquare APIs was already anonymized to remove any personally identifiable information before analysis. Note that the Foursquare data we collect strictly obeys the privacy policy in Foursquare, and the Twitter data we collect is obtained through Twitter Inc.'s Academic Research Product Track, ensuring compliance with their guidelines. The data collection and preprocessing is similar to the one in study [1]. When we construct the input of the model, the userid is a random number and the locations with GPS coordinates (e.g., Convenience Store (35.697, 139.416) ) are replaced with a location name and a unique number (e.g., Convenience Store # 1). We will include a more detailed description of these processes in the appendix of the paper next-version. [1] Yang, D., Zhang, D., & Qu, B. (2016). Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 7(3), 1-23 > **Comment 2**: *Furthermore, the paper explores tools that ease the analysis of people's movement. While the paper's "Social impacts" paragraph stresses the social utility of such tools, the paper fails to address or discuss their potential negative impacts. For example, such a tool can help track, de-identify, and target individuals at scale, raising concerns regarding their privacy and safety. I would recommend that the authors do a broader and more nuanced assessment of the potential social impacts of their work.* **Answer:** We agree with the need to provide a more detailed discussion on the potential negative impacts associated with real-world applications of our work. In this paper, we introduce one promising application of our model, which is used to simulate the urban mobility dynamic under the pandemic. In this demonstration, we only consider aggregated movement data within an area of interest, therefore individual privacy is well maintained. We also would like to point out that, as the input data is fully anonymized, there is no possibility of inferring or targeting any specific individual from the model's output. To further ensure personal privacy in other possible applications, we suggest that we further anonymize the output location depending on the specific requirements of the application.
Summary of Reviews and Rebuttals
**Table Reviewer Rating Summarization.** | Score/Reviewer | Reviewer abCw | Reviewer xLxx | Reviewer 7xtG | Reviewer c5UM | Average | | -------------- | ------------- | ----------------------------------------------------- | ------------- | ------------- | --- | | Rating | 6 | 4 | 5 | 6 | 5.25 | | Confidence | 4 | 4 | 3 | 4 | 3.75 | From the table, it is clear that **three reviewers** have been **very positive** towards our paper. Our work is the **first work** to **employ LLM agent for semantic human mobility generation**, which is a **very important and pioneering attempt in the field of LLM for computational social science**. Hope we could get your credit and kind support. Thanks a lot!
Decision
Accept (poster)