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arXiv · 2410.08996

Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference

Grace Proebsting·Adam Poliak
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Abstract

We test whether replacing crowdsource workers with LLMs to write Natural Language Inference (NLI) hypotheses similarly results in annotation artifacts. We recreate a portion of the Stanford NLI corpus using GPT-4, Llama-2 and Mistral 7b, and train hypothesis-only classifiers to determine whether LLM-elicited hypotheses contain annotation artifacts. On our LLM-elicited NLI datasets, BERT-based hypothesis-only classifiers achieve between 86-96% accuracy, indicating these datasets contain hypothesis-only artifacts. We also find frequent "give-aways" in LLM-generated hypotheses, e.g. the phrase "swimming in a pool" appears in more than 10,000 contradictions generated by GPT-4. Our analysis provides empirical evidence that well-attested biases in NLI can persist in LLM-generated data.

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Grace Proebsting, Adam Poliak. 2024-10-11. Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference. https://arxiv.org/abs/2410.08996

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