mlsanityData to 14 Oct 2026

CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims

arXiv 2012.00614 ↗First posted 1 Dec 2020cs.CL cs.AIcited by 196

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Abstract (arXiv)

We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to facilitate and encourage work on improving algorithms for retrieving evidential support for climate-specific claims, addressing the underlying language understanding challenges, and ultimately help alleviate the impact of misinformation on climate change. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. While during this process, we could rely on the expertise of renowned climate scientists, it turned out to be no easy task. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the textscfever framework, which we believe provides a valuable challenge for general natural language understanding. We hope that our work will mark the beginning of a new exciting long-term joint effort by the climate science and AI community.

Key facts. Cited by 196 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2020.

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Citing papers per year: 2021: 19; 2022: 24; 2023: 33; 2024: 46; 2025: 42; 2026: 32.

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  1. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  3. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  4. 2021Datasets: A Community Library for Natural Language Processingcited by 530
  5. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  6. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 476
  7. 2021MetaICL: Learning to Learn In Contextcited by 454
  8. 2021A Survey on Automated Fact-Checkingcited by 378
  9. 2022Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Modelscited by 180
  10. 2024GME: Improving Universal Multimodal Retrieval by Multimodal LLMscited by 170
  11. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 167
  12. 2024BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrievalcited by 158
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  1. 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
  2. 2021MultiVerS: Improving scientific claim verification with weak supervision and full-document contextcited by 143
  3. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  4. 2022Task-aware Retrieval with Instructionscited by 107
  5. 2021COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemiccited by 100
  6. 2022SciFact-Open: Towards open-domain scientific claim verificationcited by 94
  7. 2021ClimateBert: A Pretrained Language Model for Climate-Related Textcited by 90
  8. 2024BM25S: Orders of magnitude faster lexical search via eager sparse scoringcited by 88
  9. 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 87
  10. 2021Automated Fact-Checking: A Surveycited by 55
  11. 2023Active Learning Principles for In-Context Learning with Large Language Modelscited by 54
  12. 2023Scientific Fact-Checking: A Survey of Resources and Approachescited by 54
  13. 2023SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tablescited by 51
  14. 2022Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformationcited by 45
  15. 2024Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankerscited by 43
  16. 2021AmbiFC: Fact-Checking Ambiguous Claims with Evidencecited by 38
  17. 2023Language Models Hallucinate, but May Excel at Fact Verificationcited by 37
  18. 2023Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checkingcited by 35
  19. 2023Foundation Models for Weather and Climate Data Understanding: A Comprehensive Surveycited by 34
  20. 2023SAIL: Search-Augmented Instruction Learningcited by 33
  21. 2021Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlinescited by 32
  22. 2023chatClimate: Grounding Conversational AI in Climate Sciencecited by 32
  23. 2023CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Toolscited by 29
  24. 2023FactCHD: Benchmarking Fact-Conflicting Hallucination Detectioncited by 29
  25. 2021Simplified Data Wrangling with ir_datasetscited by 27
  26. 2021Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representationscited by 27
  27. 2023Interpretable Unified Language Checkingcited by 26
  28. 2021Exploring Universal Intrinsic Task Subspace via Prompt Tuningcited by 25
  29. 2022LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrievalcited by 24
  30. 2024Claim Verification in the Age of Large Language Models: A Surveycited by 24
  31. 2025MLZero: A Multi-Agent System for End-to-end Machine Learning Automationcited by 24
  32. 2023Assessing Large Language Models on Climate Informationcited by 23
  33. 2024WeatherQA: Can Multimodal Language Models Reason about Severe Weather?cited by 22
  34. 2024ClimaQA: An Automated Evaluation Framework for Climate Question Answering Modelscited by 21
  35. 2022COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learningcited by 20
  36. 2024INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuningcited by 20
  37. 2022CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrievalcited by 19
  38. 2024Generative Large Language Models in Automated Fact-Checking: A Surveycited by 19

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