During disasters, social media becomes a critical channel for identifying urgent needs and mobilizing public and institutional response efforts. Prior crisis-informatics research has mainly focused on detecting or extracting actionable content from disaster-related posts. However, real-world humanitarian response requires structured and operationally complete records, while social media posts are often short, informal, and underspecified.

In this work, we present CrisisLink, an LLM-based coordination system that transforms noisy crisis communications into structured crisis records. Unlike prior one-shot extraction systems, CrisisLink dynamically identifies missing operational fields and initiates clarification dialogues with users to repair incomplete records before downstream coordination. The system further grounds resources against a humanitarian taxonomy and supports partial, multi-item matching.

Built on the CrisisNLP corpus spanning multiple disaster types, the system is evaluated through human-audited assessments, simulated dispatch workflows, and a pilot evaluation in which trained workers role-play poster replies through a live web-based response interface. Experimental results show that a single clarification round raises end-to-end Offer–Request matching accuracy from 74.0% to 88.3% (a ceiling estimate under full response)while reducing the manual triage burden on responders, demonstrating the potential of interactive LLM systems for real-world humanitarian coordination.

Can today’s Text-to-SQL benchmarks still stretch modern LLMs? We argue no. Spider1.0 and BIRD, painstakingly hand-built, remain small, costly, and skewed toward middle complex SQL. Meanwhile, LLM-generated corpora are inexpensive but often superficial and fragile suffering from shallow nesting, semantic drift, template fatigue, and insufficient quality check.We address this gap with a Chain-of-Verifications framework that turns a handful of expert-labelled seeds into a large, reliably checked dataset at a fraction of the usual cost. The resulting corpus, AIGT2S, delivers: (1)18k Question–SQL pairs across 113 databases, 41–77% larger than current English sets; (2)55% queries in the Ultra band of our four-level difficulty taxonomy; (3)87.5% inter-annotator agreement; (4)≥80% labour and ≥98% monetary savings versus earlier efforts.Baselines including GPT-4o, Llama3, RESDSQL, and MAC-SQL, achieve at most 56% execution accuracy, indicating substantial room for improvement.