Datasets:

rlacombe
/

ClimateX

@@ -1,13 +1,13 @@
 # IPCC Confidence in Climate Statements
 _What do LLMs know about climate? Let's find out!_
-### ICCS Dataset
 We introduce the **ICCS dataset (IPCC Confidence in Climate Statements)** is a novel, curated, expert-labeled, natural language dataset of 8094 statements extracted or paraphrased from the IPCC Assessment Report 6: [Working Group I report](https://www.ipcc.ch/report/ar6/wg1/), [Working Group II report](https://www.ipcc.ch/report/ar6/wg2/), and [Working Group III report](https://www.ipcc.ch/report/ar6/wg3/), respectively.
 Each statement is labeled with the corresponding IPCC report source, the page number in the report PDF, and the corresponding confidence level (, along with their associated confidence levels (`low`, `medium`, `high`, or `very high`) as assessed by IPCC climate scientists based on available evidence and agreement among their peers.
-### Dataset Construction
 To construct the dataset, we retrieved the complete raw text from each of the three IPCC report PDFs that are available online using an open-source library [pypdf2](https://pypi.org/project/PyPDF2/). We then normalized the whitespace, tokenized the text into sentences using [NLTK](https://www.nltk.org/) , and used regex search to filter for complete sentences including a parenthetical confidence label at the end of the statement, of the form _sentence (low|medium|high|very high confidence)_. The final ICCS dataset contains 8094 labeled sentences.
@@ -21,17 +21,30 @@ Then, we manually reviewed and cleaned each sentence in the test set to provide
 - We split 19 compound statements with conflicting confidence sub-labels, and removed 6 extraneous mid-sentence labels of the same category as the end-of-sentence label;
 - We added light context to 23 sentences, and replaced 5 sentences by others when they were meaningless outside of a longer paragraph;
 - We removed qualifiers at the beginning of 29 sentences to avoid biasing classification (e.g. 'But...', 'In summary...', 'However...').
-\end{itemize}
 **The remaining 7794 sentences not allocated to the test split form our train split.**
 Of note: while the IPCC report uses a 5 levels scale for confidence, almost no `very low confidence` statement makes it through the peer review process to the final reports, such that no statement of the form _sentence (very low confidence)_ was retrievable. Therefore, we chose to build our data set with only statements labeled as `low`, `medium`, `high` and `very high` confidence.
-### Code Download
 The code to reproduce dataset collection and our LLM benchmarking experiments is avalaible on [GitHub](https://github.com/rlacombe/Climate-LLMs).
-### Paper ###
 We use this dataset to evaluate how recent LLMs fare at classifying the  scientific confidence associated with each statement in a statistically representative, carefully constructed test split of the dataset.

 # IPCC Confidence in Climate Statements
 _What do LLMs know about climate? Let's find out!_
+## ICCS Dataset
 We introduce the **ICCS dataset (IPCC Confidence in Climate Statements)** is a novel, curated, expert-labeled, natural language dataset of 8094 statements extracted or paraphrased from the IPCC Assessment Report 6: [Working Group I report](https://www.ipcc.ch/report/ar6/wg1/), [Working Group II report](https://www.ipcc.ch/report/ar6/wg2/), and [Working Group III report](https://www.ipcc.ch/report/ar6/wg3/), respectively.
 Each statement is labeled with the corresponding IPCC report source, the page number in the report PDF, and the corresponding confidence level (, along with their associated confidence levels (`low`, `medium`, `high`, or `very high`) as assessed by IPCC climate scientists based on available evidence and agreement among their peers.
+## Dataset Construction
 To construct the dataset, we retrieved the complete raw text from each of the three IPCC report PDFs that are available online using an open-source library [pypdf2](https://pypi.org/project/PyPDF2/). We then normalized the whitespace, tokenized the text into sentences using [NLTK](https://www.nltk.org/) , and used regex search to filter for complete sentences including a parenthetical confidence label at the end of the statement, of the form _sentence (low|medium|high|very high confidence)_. The final ICCS dataset contains 8094 labeled sentences.
 - We split 19 compound statements with conflicting confidence sub-labels, and removed 6 extraneous mid-sentence labels of the same category as the end-of-sentence label;
 - We added light context to 23 sentences, and replaced 5 sentences by others when they were meaningless outside of a longer paragraph;
 - We removed qualifiers at the beginning of 29 sentences to avoid biasing classification (e.g. 'But...', 'In summary...', 'However...').
 **The remaining 7794 sentences not allocated to the test split form our train split.**
 Of note: while the IPCC report uses a 5 levels scale for confidence, almost no `very low confidence` statement makes it through the peer review process to the final reports, such that no statement of the form _sentence (very low confidence)_ was retrievable. Therefore, we chose to build our data set with only statements labeled as `low`, `medium`, `high` and `very high` confidence.
+## Dataset Card
+---
+language:
+- en
+pretty_name: "ICCS (IPCC Confidence in Climate Statements)"
+tags:
+- climate
+- nlp
+license: mit
+task_categories:
+- classification
+---
+## Code Download
 The code to reproduce dataset collection and our LLM benchmarking experiments is avalaible on [GitHub](https://github.com/rlacombe/Climate-LLMs).
+## Paper
 We use this dataset to evaluate how recent LLMs fare at classifying the  scientific confidence associated with each statement in a statistically representative, carefully constructed test split of the dataset.