Large Language Models are Zero-Shot Next Location Predictors

• URL: http://arxiv.org/abs/2405.20962v3
• Date: Fri, 23 Aug 2024 09:24:22 GMT
• Title: Large Language Models are Zero-Shot Next Location Predictors
• Authors: Ciro Beneduce, Bruno Lepri, Massimiliano Luca,
• Abstract summary: Large Language Models (LLMs) have shown good generalization and reasoning capabilities. LLMs can obtain accuracies up to 36.2%, a significant improvement of almost 640% when compared to other models specifically designed for human mobility.
• Score: 4.315451628809687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and reduction of pollution. However, next-location predictors require a significant amount of individual-level information that may be scarce or unavailable in some scenarios (e.g., cold-start). Large Language Models (LLMs) have shown good generalization and reasoning capabilities and are rich in geographical knowledge, allowing us to believe that these models can act as zero-shot next-location predictors. We tested more than 15 LLMs on three real-world mobility datasets and we found that LLMs can obtain accuracies up to 36.2%, a significant relative improvement of almost 640% when compared to other models specifically designed for human mobility. We also test for data contamination and explored the possibility of using LLMs as text-based explainers for next-location prediction, showing that, regardless of the model size, LLMs can explain their decision.

Related papers

• Predicting Emergent Capabilities by Finetuning [98.9684114851891]
  We find that finetuning language models can shift the point in scaling at which emergence occurs towards less capable models. We validate this approach using four standard NLP benchmarks. We find that, in some cases, we can accurately predict whether models trained with up to 4x more compute have emerged.
  arXiv  Detail & Related papers  (2024-11-25T01:48:09Z)
• AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction [7.007450097312181]
  We introduce AgentMove, a systematic agentic prediction framework to achieve generalized next location prediction. In AgentMove, we first decompose the mobility prediction task and design specific modules to complete them, including spatial-temporal memory for individual mobility pattern mining. Experiments utilizing mobility data from two distinct sources reveal that AgentMove surpasses the leading baseline by 3.33% to 8.57% across 8 out of 12 metrics.
  arXiv  Detail & Related papers  (2024-08-26T02:36:55Z)
• Deep Bayesian Active Learning for Preference Modeling in Large Language Models [84.817400962262]
  We propose the Bayesian Active Learner for Preference Modeling (BAL-PM) for Preference Modeling. BAL-PM requires 33% to 68% fewer preference labels in two popular human preference datasets and exceeds previous Bayesian acquisition policies. Our experiments demonstrate that BAL-PM requires 33% to 68% fewer preference labels in two popular human preference datasets and exceeds previous Bayesian acquisition policies.
  arXiv  Detail & Related papers  (2024-06-14T13:32:43Z)
• Bayesian Statistical Modeling with Predictors from LLMs [5.5711773076846365]
  State of the art large language models (LLMs) have shown impressive performance on a variety of benchmark tasks. This raises questions about the human-likeness of LLM-derived information.
  arXiv  Detail & Related papers  (2024-06-13T11:33:30Z)
• Large Language Models Must Be Taught to Know What They Don't Know [97.90008709512921]
  We show that fine-tuning on a small dataset of correct and incorrect answers can create an uncertainty estimate with good generalization and small computational overhead. We also investigate the mechanisms that enable reliable uncertainty estimation, finding that many models can be used as general-purpose uncertainty estimators.
  arXiv  Detail & Related papers  (2024-06-12T16:41:31Z)
• Low-rank finetuning for LLMs: A fairness perspective [54.13240282850982]
  Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. We show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors.
  arXiv  Detail & Related papers  (2024-05-28T20:43:53Z)
• Distortions in Judged Spatial Relations in Large Language Models [45.875801135769585]
  GPT-4 exhibited superior performance with 55 percent accuracy, followed by GPT-3.5 at 47 percent, and Llama-2 at 45 percent. The models identified the nearest cardinal direction in most cases, reflecting their associative learning mechanism.
  arXiv  Detail & Related papers  (2024-01-08T20:08:04Z)
• Where Would I Go Next? Large Language Models as Human Mobility Predictors [21.100313868232995]
  We introduce a novel method, LLM-Mob, which leverages the language understanding and reasoning capabilities of LLMs for analysing human mobility data. Comprehensive evaluations of our method reveal that LLM-Mob excels in providing accurate and interpretable predictions.
  arXiv  Detail & Related papers  (2023-08-29T10:24:23Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)
• CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models [3.682742580232362]
  Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields. Our research is the first to tackle drug pair synergy prediction in rare tissues with limited data.
  arXiv  Detail & Related papers  (2023-04-18T02:49:53Z)
• Holistic Evaluation of Language Models [183.94891340168175]
  Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
  arXiv  Detail & Related papers  (2022-11-16T18:51:34Z)

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.