Large Language Models are Zero-Shot Next Location Predictors
- URL: http://arxiv.org/abs/2405.20962v2
- Date: Mon, 3 Jun 2024 15:10:53 GMT
- Title: Large Language Models are Zero-Shot Next Location Predictors
- Authors: Ciro Beneduce, Bruno Lepri, Massimiliano Luca,
- Abstract summary: Large Language Models (LLMs) can act as zero-shot next-location predictors.
LLMs can obtain accuracies up to 32.4%, a significant improvement of over 600% when compared to sophisticated DL models.
We propose a framework inspired by other studies to test data contamination.
- 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 among many others. The models designed to tackle next-location prediction, however, require a significant amount of individual-level information to be trained effectively. Such data may be scarce or even unavailable in some geographic regions or peculiar scenarios (e.g., cold-start in recommendation systems). Moreover, the design of a next-location predictor able to generalize or geographically transfer knowledge is still an open research challenge. Recent advances in natural language processing have led to a rapid diffusion of Large Language Models (LLMs) which have shown good generalization and reasoning capabilities. These insights, coupled with the recent findings that LLMs are rich in geographical knowledge, allowed us to believe that these models can act as zero-shot next-location predictors. This paper evaluates the capabilities of many popular LLMs in this role, specifically Llama, GPT-3.5 and Mistral 7B. After designing a proper prompt, we tested the models on three real-world mobility datasets. The results show that LLMs can obtain accuracies up to 32.4%, a significant relative improvement of over 600% when compared to sophisticated DL models specifically designed for human mobility. Moreover, we show that other LLMs are unable to perform the task properly. To prevent positively biased results, we also propose a framework inspired by other studies to test data contamination. Finally, we explored the possibility of using LLMs as text-based explainers for next-location prediction showing that can effectively provide an explanation for their decision. Notably, 7B models provide more generic, but still reliable, explanations compared to larger counterparts. Code: github.com/ssai-trento/LLM-zero-shot-NL
Related papers
- LLM-Select: Feature Selection with Large Language Models [64.5099482021597]
Large language models (LLMs) are capable of selecting the most predictive features, with performance rivaling the standard tools of data science.
Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place.
arXiv Detail & Related papers (2024-07-02T22:23:40Z) - Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach [6.913791588789051]
We introduce a novel prompt-learning-based Large Language Model(LLM) framework that significantly improves prediction accuracy and provides explicit explanations for individual predictions.
We tested the framework's efficacy using two widely used choice datasets: London Passenger Mode Choice (LPMC) and Optima-Mode collected in Switzerland.
The results indicate that the LLM significantly outperforms state-of-the-art deep learning methods and discrete choice models in predicting people's choices.
arXiv Detail & Related papers (2024-06-19T13:46:08Z) - Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation [28.610190512686767]
Next Point-of-interest (POI) recommendation provides valuable suggestions for users to explore their surrounding environment.
Existing studies rely on building recommendation models from large-scale users' check-in data.
Recently, the pretrained large language models (LLMs) have achieved significant advancements in various NLP tasks.
arXiv Detail & Related papers (2024-04-02T11:33:04Z) - 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) - ROBBIE: Robust Bias Evaluation of Large Generative Language Models [27.864027322486375]
Different prompt-based datasets can be used to measure social bias across multiple text domains and demographic axes.
We compare 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs.
We conduct a comprehensive study of how well 3 bias/toxicity mitigation techniques perform across our suite of measurements.
arXiv Detail & Related papers (2023-11-29T23:03:04Z) - 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) - Is ChatGPT Good at Search? Investigating Large Language Models as
Re-Ranking Agents [56.104476412839944]
Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization across various language-related tasks.
This paper investigates generative LLMs for relevance ranking in Information Retrieval (IR)
To address concerns about data contamination of LLMs, we collect a new test set called NovelEval.
To improve efficiency in real-world applications, we delve into the potential for distilling the ranking capabilities of ChatGPT into small specialized models.
arXiv Detail & Related papers (2023-04-19T10:16:03Z) - Large Language Models Are Latent Variable Models: Explaining and Finding
Good Demonstrations for In-Context Learning [104.58874584354787]
In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning.
This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
arXiv Detail & Related papers (2023-01-27T18:59:01Z) - Augmenting Interpretable Models with LLMs during Training [73.40079895413861]
We propose Augmented Interpretable Models (Aug-imodels) to build efficient and interpretable models.
Aug-imodels use LLMs during fitting but not during inference, allowing complete transparency.
We explore two instantiations of Aug-imodels in natural-language processing: (i) Aug-GAM, which augments a generalized additive model with decoupled embeddings from an LLM and (ii) Aug-Tree, which augments a decision tree with LLM feature expansions.
arXiv Detail & Related papers (2022-09-23T18:36:01Z)
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.