What can LLM tell us about cities?
- URL: http://arxiv.org/abs/2411.16791v1
- Date: Mon, 25 Nov 2024 09:07:56 GMT
- Title: What can LLM tell us about cities?
- Authors: Zhuoheng Li, Yaochen Wang, Zhixue Song, Yuqi Huang, Rui Bao, Guanjie Zheng, Zhenhui Jessie Li,
- Abstract summary: This study explores the capabilities of large language models (LLMs) in providing knowledge about cities and regions on a global scale.
Experiments reveal that LLMs embed a broad but varying degree of knowledge across global cities, with ML models trained on LLM-derived features consistently leading to improved predictive accuracy.
- Score: 6.405546719612814
- License:
- Abstract: This study explores the capabilities of large language models (LLMs) in providing knowledge about cities and regions on a global scale. We employ two methods: directly querying the LLM for target variable values and extracting explicit and implicit features from the LLM correlated with the target variable. Our experiments reveal that LLMs embed a broad but varying degree of knowledge across global cities, with ML models trained on LLM-derived features consistently leading to improved predictive accuracy. Additionally, we observe that LLMs demonstrate a certain level of knowledge across global cities on all continents, but it is evident when they lack knowledge, as they tend to generate generic or random outputs for unfamiliar tasks. These findings suggest that LLMs can offer new opportunities for data-driven decision-making in the study of cities.
Related papers
- WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents [55.64361927346957]
We propose a neurosymbolic approach to learn rules gradient-free through large language models (LLMs)
Our embodied LLM agent "WALL-E" is built upon model-predictive control (MPC)
On open-world challenges in Minecraft and ALFWorld, WALL-E achieves higher success rates than existing methods.
arXiv Detail & Related papers (2024-10-09T23:37:36Z) - Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data [9.31120925026271]
We study inductive out-of-context reasoning (OOCR) in which LLMs infer latent information from evidence distributed across training documents.
In one experiment we finetune an LLM on a corpus consisting only of distances between an unknown city and other known cities.
While OOCR succeeds in a range of cases, we also show that it is unreliable, particularly for smaller LLMs learning complex structures.
arXiv Detail & Related papers (2024-06-20T17:55:04Z) - Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model.
We employ a proxy model which has far fewer parameters, and take its answers as answers.
Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
arXiv Detail & Related papers (2024-02-19T11:11:08Z) - Large Language Models: A Survey [69.72787936480394]
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks.
LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
arXiv Detail & Related papers (2024-02-09T05:37:09Z) - See the Unseen: Better Context-Consistent Knowledge-Editing by Noises [73.54237379082795]
Knowledge-editing updates knowledge of large language models (LLMs)
Existing works ignore this property and the editing lacks generalization.
We empirically find that the effects of different contexts upon LLMs in recalling the same knowledge follow a Gaussian-like distribution.
arXiv Detail & Related papers (2024-01-15T09:09:14Z) - Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering.
The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored.
We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
arXiv Detail & Related papers (2023-12-26T07:24:46Z) - LLaMA Rider: Spurring Large Language Models to Explore the Open World [36.261626047323695]
The capacity of Large Language Models to continuously acquire environmental knowledge and adapt in an open world remains uncertain.
We propose an approach to spur LLMs to explore the open world, gather experiences, and learn to improve their task-solving capabilities.
By evaluation in Minecraft, an open-ended sandbox world, we demonstrate that our approach LLaMA-Rider enhances the efficiency of the LLM in exploring the environment.
arXiv Detail & Related papers (2023-10-13T07:47:44Z) - Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well.
Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries.
We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
arXiv Detail & Related papers (2023-07-20T16:46:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.