Papers with hypothesis-only classifiers
MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain (2021.acl-short)
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| Challenge: | a large number of crowdworker-constructed datasets have been used to conduct natural language inference (NLI) on unstructured, domainspecific texts such as patient notes, pathology reports, and scientific papers. |
| Approach: | They investigate whether MedNLI contains lexical and syntactic annotation artifacts associated with annotation process that allow hypothesis-only classifiers to achieve better-than-random performance. |
| Outcome: | The proposed model outperforms a majority-class baseline model on a physician-annotated dataset with premises extracted from clinical notes. |
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. |