Fine-tuned GPT-3.5-turbo agrees with expert climate coders on social media claims as often as the experts agree with each other (alpha=0.89), but the study's open-source benchmark is weakened by a flawed prompt and random replacement of invalid responses.
Towards Fine-grained Classification of Climate Change related Social Media Text
3 Pith papers cite this work, alongside 19 external citations. Polarity classification is still indexing.
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Pith papers citing it
19
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representative citing papers
Reasoning models detect modifications to their chains of thought with only modest accuracy and cannot reliably identify the nature of those modifications.
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
citing papers explorer
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Enhancing LLMs for Governance with Human Oversight: Evaluating and Aligning LLMs on Expert Classification of Climate Misinformation for Detecting False or Misleading Claims about Climate Change
Fine-tuned GPT-3.5-turbo agrees with expert climate coders on social media claims as often as the experts agree with each other (alpha=0.89), but the study's open-source benchmark is weakened by a flawed prompt and random replacement of invalid responses.
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Can Reasoning Models Detect Changes to their Chains of Thought?
Reasoning models detect modifications to their chains of thought with only modest accuracy and cannot reliably identify the nature of those modifications.
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Task Decomposition for Efficient Annotation
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.