# Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs

arXiv: https://arxiv.org/abs/2504.16144 · HTML: https://mlsanity.com/paper/2504.16144/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis](https://mlsanity.com/paper/2311.00237/) (2023)
- [Utilizing Microblogs for Assisting Post-Disaster Relief Operations via Matching Resource Needs and Availabilities](https://mlsanity.com/paper/2007.09368/) (2020)
- [Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features](https://mlsanity.com/paper/2412.10413/) (2024)
- [RweetMiner: Automatic identification and categorization of help requests on twitter during disasters](https://mlsanity.com/paper/2303.02399/) (2023)
- [CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media](https://mlsanity.com/paper/2405.11897/) (2024)
- [Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning](https://mlsanity.com/paper/2211.11468/) (2022)
- [Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models](https://mlsanity.com/paper/2404.14432/) (2024)

## Built on by (most cited first, 3 of 3)

- [GeoResponder: Towards Building Geospatial LLMs for Time-Critical Disaster Response](https://mlsanity.com/paper/2509.19354/) (2025)
- [A Dynamic Fusion Model for Consistent Crisis Response](https://mlsanity.com/paper/2509.01053/) (2025)
- [CrisisFake: Benchmark Validity of AI-Generated Text Detection for Disaster Social Sensing](https://mlsanity.com/paper/2609.35821/) (2026)

## Abstract (arXiv)

Natural disasters often result in a surge of social media activity, including requests for assistance, offers of help, sentiments, and general updates. To enable humanitarian organizations to respond more efficiently, we propose a fine-grained hierarchical taxonomy to systematically organize crisis-related information about requests and offers into three critical dimensions: supplies, emergency personnel, and actions. Leveraging the capabilities of Large Language Models (LLMs), we introduce Query-Specific Few-shot Learning (QSF Learning) that retrieves class-specific labeled examples from an embedding database to enhance the model's performance in detecting and classifying posts. Beyond classification, we assess the actionability of messages to prioritize posts requiring immediate attention. Extensive experiments demonstrate that our approach outperforms baseline prompting strategies, effectively identifying and prioritizing actionable requests and offers.