mlsanityData to 14 Oct 2026

chatClimate: Grounding Conversational AI in Climate Science

arXiv 2304.05510 ↗First posted 11 Apr 2023cs.CLcited by 32

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Abstract (arXiv)

Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase. These challenges take center stage in critical domains like climate change, where obtaining accurate and up-to-date information from reliable sources in a limited time is essential and difficult. To overcome these barriers, one potential solution is to provide LLMs with access to external, scientifically accurate, and robust sources (long-term memory) to continuously update their knowledge and prevent the propagation of inaccurate, incorrect, or outdated information. In this study, we enhanced GPT-4 by integrating the information from the Sixth Assessment Report of the Intergovernmental (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain. We present our conversational AI prototype, available at www.chatclimate.ai and demonstrate its ability to answer challenging questions accurately in three different QA scenarios: asking from 1) GPT-4, 2) chatClimate, and 3) hybrid chatClimate. The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high). The evaluation showed that the hybrid chatClimate provided more accurate answers, highlighting the effectiveness of our solution. This approach can be easily scaled for chatbots in specific domains, enabling the delivery of reliable and accurate information.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 34 of its 80 references that are in the map. First posted on arXiv 11 Apr 2023.

Builds on

34 of 80 references.

References · 34

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22 more
  1. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 13; 2025: 12; 2026: 1.

32 papers.

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  1. 2023GPT4GEO: How a Language Model Sees the World's Geographycited by 68
  2. 2023Foundation Models for Weather and Climate Data Understanding: A Comprehensive Surveycited by 34
  3. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  4. 2023CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Toolscited by 29
  5. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 24
  6. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  7. 2024WeatherQA: Can Multimodal Language Models Reason about Severe Weather?cited by 22
  8. 2024From Text to Transformation: A Comprehensive Review of Large Language Models' Versatilitycited by 19
  9. 2024WildfireGPT: Tailored Large Language Model for Wildfire Analysiscited by 15
  10. 2025Accelerating Earth Science Discovery via Multi-Agent LLM Systemscited by 13
  11. 2024ClimateIQA: A New Dataset and Benchmark to Advance Vision-Language Models in Meteorology Anomalies Analysiscited by 10
  12. 2024Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptationcited by 7
20 more
  1. 2023Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Modelscited by 6
  2. 2023Climate Change from Large Language Modelscited by 6
  3. 2024ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolationcited by 6
  4. 2025Don't Think Twice! Over-Reasoning Impairs Confidence Calibrationcited by 6
  5. 2025A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptationcited by 5
  6. 2024Automated Fact-Checking of Climate Change Claims with Large Language Modelscited by 4
  7. 2024Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategycited by 4
  8. 2024Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goalscited by 3
  9. 2024AI for Extreme Event Modeling and Understanding: Methodologies and Challengescited by 3
  10. 2025ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queriescited by 3
  11. 2026ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AIcited by 3
  12. 2025Urban Computing in the Era of Large Language Modelscited by 2
  13. 2025Knowledge-Grounded Agentic Large Language Models for Multi-Hazard Understanding from Reconnaissance Reportscited by 2
  14. 2024Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Eventscited by 1
  15. 2025Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Modelscited by 1
  16. 2025GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?cited by 1
  17. 2023ChatSOS: LLM-based knowledge Q&A system for safety engineeringcited by 0
  18. 2025ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analyticscited by 0
  19. 2025CLINB: A Climate Intelligence Benchmark for Foundational Modelscited by 0
  20. 2025EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detectioncited by 0