# CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics

arXiv: https://arxiv.org/abs/2406.15477 · HTML: https://mlsanity.com/paper/2406.15477/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 64 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://mlsanity.com/paper/2308.08747/) (2023)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?](https://mlsanity.com/paper/2204.05832/) (2022)
- [Text Classification via Large Language Models](https://mlsanity.com/paper/2305.08377/) (2023)
- [CrisisMMD: Multimodal Twitter Datasets from Natural Disasters](https://mlsanity.com/paper/1805.00713/) (2018)
- [Transcending Scaling Laws with 0.1% Extra Compute](https://mlsanity.com/paper/2210.11399/) (2022)
- [Exploring Large Language Models for Human Mobility Prediction under Public Events](https://mlsanity.com/paper/2311.17351/) (2023)
- [Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following](https://mlsanity.com/paper/2302.14691/) (2023)
- [Prompting Frameworks for Large Language Models: A Survey](https://mlsanity.com/paper/2311.12785/) (2023)
- [Examining Scaling and Transfer of Language Model Architectures for Machine Translation](https://mlsanity.com/paper/2202.00528/) (2022)
- [Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning](https://mlsanity.com/paper/2106.03103/) (2021)
- [Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas](https://mlsanity.com/paper/2309.14610/) (2023)

## Built on by (most cited first, 25 of 25)

- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs](https://mlsanity.com/paper/2502.17701/) (2025)
- [A Survey of Scaling in Large Language Model Reasoning](https://mlsanity.com/paper/2504.02181/) (2025)
- [DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response](https://mlsanity.com/paper/2410.20707/) (2024)
- [Can LLMs Understand Social Norms in Autonomous Driving Games?](https://mlsanity.com/paper/2408.12680/) (2024)
- [Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research](https://mlsanity.com/paper/2411.00890/) (2024)
- [Disaster Management in the Era of Agentic AI Systems: A Vision for Collective Human-Machine Intelligence for Augmented Resilience](https://mlsanity.com/paper/2510.16034/) (2025)
- [Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches](https://mlsanity.com/paper/2512.12677/) (2025)
- [DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management](https://mlsanity.com/paper/2505.15856/) (2025)
- [A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation](https://mlsanity.com/paper/2506.03360/) (2025)
- [CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment](https://mlsanity.com/paper/2602.13239/) (2026)
- [How and Where to Translate? The Impact of Translation Strategies in Cross-lingual LLM Prompting](https://mlsanity.com/paper/2507.22923/) (2025)
- [DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management](https://mlsanity.com/paper/2510.15087/) (2025)
- [OnRL-RAG: Real-Time Personalized Mental Health Dialogue System](https://mlsanity.com/paper/2504.02894/) (2025)
- [ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics](https://mlsanity.com/paper/2504.19066/) (2025)
- [A Dynamic Fusion Model for Consistent Crisis Response](https://mlsanity.com/paper/2509.01053/) (2025)
- [From Detection to Mitigation: Addressing Gender Bias in Chinese Texts via Efficient Tuning and Voting-Based Rebalancing](https://mlsanity.com/paper/2509.07889/) (2025)
- [Learning to vary: Teaching LMs to reproduce human linguistic variability in next-word prediction](https://mlsanity.com/paper/2509.17794/) (2025)
- [A Lightweight LLM Framework for Disaster Humanitarian Information Classification](https://mlsanity.com/paper/2602.12284/) (2026)
- [Cross-Modal Rationale Transfer for Explainable Humanitarian Classification on Social Media](https://mlsanity.com/paper/2603.18611/) (2026)
- [CBRS: Cognitive Blood Request System with Bilingual Dataset and Dual-Layer Filtering for Multi-Platform Social Streams](https://mlsanity.com/paper/2604.16665/) (2026)
- [LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification](https://mlsanity.com/paper/2605.08448/) (2026)
- [Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support](https://mlsanity.com/paper/2606.00991/) (2026)
- [Guide Me Out: A Framework to Benchmark VLM Operators Communication in Crisis Scenarios](https://mlsanity.com/paper/2606.09428/) (2026)
- [CrisisFake: Benchmark Validity of AI-Generated Text Detection for Disaster Social Sensing](https://mlsanity.com/paper/2609.35821/) (2026)

## Abstract (arXiv)

In the field of crisis/disaster informatics, social media is increasingly being used for improving situational awareness to inform response and relief efforts. Efficient and accurate text classification tools have been a focal area of investigation in crisis informatics. However, current methods mostly rely on single-label text classification models, which fails to capture different insights embedded in dynamic and multifaceted disaster-related social media data. This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM) through instruction fine-tuning targeted for multi-label classification of disaster-related tweets. Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM, thereby embedding it with disaster-specific knowledge. This fine-tuned model can classify multiple aspects of disaster-related information simultaneously, such as the type of event, informativeness, and involvement of human aid, significantly improving the utility of social media data for situational awareness in disasters. The results demonstrate that this approach enhances the categorization of critical information from social media posts, thereby facilitating a more effective deployment for situational awareness during emergencies. This research paves the way for more advanced, adaptable, and robust disaster management tools, leveraging the capabilities of LLMs to improve real-time situational awareness and response strategies in disaster scenarios.