Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference (2025.coling-main)
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| Challenge: | Creating NLP datasets with Large Language Models (LLMs) is an attractive alternative to relying on crowd-source workers. |
| Approach: | They recreate a portion of the Stanford Natural Language Inference corpus using GPT-4, Llama-2 70b for Chat, and Mistral 7b Instruct. |
| Outcome: | The proposed model can be used to generate NLP datasets with stereotypical biases and annotation artifacts. |
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