RALLM-POI: Retrieval-Augmented LLM for Zero-shot Next POI Recommendation with Geographical Reranking

2025-09-21 · Kunrong Li, Kwan Hui Lim arxiv

Next point-of-interest (POI) recommendation predicts a user's next destination from historical movements. Traditional models require intensive training, while LLMs offer flexible and generalizable zero-shot solutions but often generate generic or geographically irrelevant results due to missing trajectory and spatial context. To address these issues, we propose RALLM-POI, a framework that couples LLMs with retrieval-augmented generation and self-rectification. We first propose a Historical Trajectory Retriever (HTR) that retrieves relevant past trajectories to serve as contextual references, which are then reranked by a Geographical Distance Reranker (GDR) for prioritizing spatially relevant trajectories. Lastly, an Agentic LLM Rectifier (ALR) is designed to refine outputs through self-reflection. Without additional training, RALLM-POI achieves substantial accuracy gains across three real-world Foursquare datasets, outperforming both conventional and LLM-based baselines. Code is released at https://github.com/LKRcrocodile/RALLM-POI.

📄 PDF Abstract BibTeX arXiv:2509.17066

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R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models

2024-06-17 · Shangqing Tu, Yuanchun Wang, Jifan Yu, Yuyang Xie 외

Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems. Recently, various Retrieval-Augmented Large Language Models (RALLMs) are proposed to addr…

RAG Retrieval

Audiobox TTA-RAG: Improving Zero-Shot and Few-Shot Text-To-Audio with Retrieval-Augmented Generation

2024-11-07 · Mu Yang, Bowen Shi, Matthew Le, Wei-Ning Hsu 외

This work focuses on improving Text-To-Audio (TTA) generation on zero-shot and few-shot settings (i.e. generating unseen or uncommon audio events). Inspired by the success of Retrieval-Augmented Generation (RAG) in Large…

Audio Generation Large Language Model RAG Retrieval+1

TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster

2025-03-06 · Kanghui Ning, Zijie Pan, Yu Liu, Yushan Jiang 외

Recently, Large Language Models (LLMs) and Foundation Models (FMs) have become prevalent for time series forecasting tasks. However, fine-tuning large language models (LLMs) for forecasting enables the adaptation to spec…

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GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval

2023-10-31 · Daman Arora, Anush Kini, Sayak Ray Chowdhury, Nagarajan Natarajan 외

Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents. Combining large language models (LLMs) with embedding-based retrieval models, recent work shows p…

Information Retrieval Passage Retrieval RAG Re-Ranking+2

TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting

2024-12-30 · Huanyu Zhang, Chang Xu, Yi-Fan Zhang, Zhang Zhang 외

Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable gene…

RAG Retrieval Retrieval-augmented Generation Time Series+2