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Related Questions
- Can active learning be used to identify the most uncertain instances in LLMs' counterfactual reasoning, allowing for targeted annotation and improvement of explainability?
- How can active learning algorithms be designed to select instances that provide the most informative feedback for improving LLMs' counterfactual reasoning and explainability?
- In what ways can active learning be used to reduce the annotation burden and improve the efficiency of improving LLMs' counterfactual reasoning and explainability?
- Can active learning be used to identify biases in LLMs' counterfactual reasoning and improve their explainability by selectively sampling instances that highlight these biases?
- How can active learning be integrated with other techniques, such as model interpretability methods, to improve the explainability of LLMs' counterfactual reasoning?
- Can active learning be used to improve the generalizability of LLMs' counterfactual reasoning by selectively sampling instances from diverse domains and scenarios?
- What are the potential challenges and limitations of using active learning to improve the explainability of LLMs' counterfactual reasoning, and how can they be addressed?
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